Patent Publication Number: US-9888851-B2

Title: Hand-held medical-data capture-device having determination of a temperature by a microprocessor from a signal from a digital infrared sensor having only digital readout ports and the digital infrared sensor having no analog sensor readout ports and having interoperation with electronic medical record systems on a specific segment of a network to transmit the temperature and device information

Description:
RELATED APPLICATION 
     This application is a continuation of, and claims the benefit and priority under 35 U.S.C. 120 of U.S. Original patent application Ser. No. 14/523,890 filed 25 Oct. 2014, which is hereby incorporated by reference in its entirety. 
    
    
     FIELD 
     This disclosure relates generally to transmitting a representation of animal body core temperature or other vital signs to an electronic medical record system. 
     BACKGROUND 
     Hand-held medical-data capture-devices are stand-alone devices in which data is retrieved from the device by an operator who reads a temperature or other vital sign from a display screen in the hand-held medical-data capture-devices and who then manually records the vital sign in an electronic medical record system, which is a very slow and expensive process. 
     BRIEF DESCRIPTION 
     In one aspect, an apparatus estimates body core temperature from an infrared measurement of an external source point using a cubic relationship between the body core temperature and the measurement of an external source point and transmits the apparatus estimate of body core temperature to an external device. 
     In a further aspect, a non-touch biologic detector estimates body core temperature from an infrared measurement of an external source point and determines vital signs from a solid-state image transducer and transmits the apparatus estimate of body core temperature and the vital sign to an external device. 
     In another aspect, a non-touch biologic detector determines vital signs from a solid-state image transducer and estimates body core temperature from an infrared measurement of an external source point using a cubic relationship between the body core temperature and the measurement of an external source point and transmits the apparatus estimate of body core temperature and the vital sign to an external device. 
     Apparatus, systems, and methods of varying scope are described herein. In addition to the aspects and advantages described in this summary, further aspects and advantages will become apparent by reference to the drawings and by reading the detailed description that follows. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an overview of an electronic medical records (EMR) capture system, according to an implementation; 
         FIG. 2  is a block diagram of an overview of an EMR capture system having a remote cloud based bridge, according to an implementation; 
         FIG. 3  is a block diagram of a non-touch biologic detector that includes a digital infrared sensor, according to an implementation; 
         FIG. 4  is a block diagram of a non-touch biologic detector that includes a digital infrared sensor and that does not include an analog-to-digital converter, according to an implementation; 
         FIG. 5  is a block diagram of a non-touch biologic detector that includes a digital infrared sensor and a color display device, according to an implementation; 
         FIG. 6  is a block diagram of apparatus that estimates a body core temperature of an external source point from a no-touch electromagnetic sensor, according to an implementation; 
         FIG. 7  is a block diagram of apparatus to estimate a body core temperature from an external source point from an analog infrared sensor, according to an implementation; 
         FIG. 8  is a block diagram of apparatus to estimate a body core temperature from an external source point from a digital infrared sensor, according to an implementation; 
         FIG. 9  is a block diagram of apparatus that estimates a body core temperature of an external source point from a non-touch electromagnetic sensor and that detects vital-signs from images captured by a solid-state image transducer, according to an implementation; 
         FIG. 10  is a block diagram of apparatus that estimates a body core temperature of an external source point from an analog infrared sensor and that detects vital-signs from images captured by a solid-state image transducer, according to an implementation. 
         FIG. 11  is a block diagram of apparatus that estimates a body core temperature of an external source point from a digital infrared sensor and that detects vital-signs from images captured by a solid-state image transducer, according to an implementation; 
         FIG. 12  is a block diagram of apparatus that estimates a body core temperature of an external source point from a digital infrared sensor, that does not include an analog-to-digital converter and that detects vital-signs from images captured by a solid-state image transducer, according to an implementation; 
         FIG. 13  is a flowchart of a method to determine a temperature from a digital infrared sensor, according to an implementation; 
         FIG. 14  is a flowchart of a method to display temperature color indicators, according to an implementation of three colors; 
         FIG. 15  is a flowchart of a method to manage power in a non-touch biologic detector or thermometer having a digital infrared sensor, according to an implementation; 
         FIG. 16  is a block diagram of an apparatus of variation amplification, according to an implementation. 
         FIG. 17  is a block diagram of an apparatus of variation amplification, according to an implementation. 
         FIG. 18  is a block diagram of an apparatus of variation amplification, according to an implementation. 
         FIG. 19  is a block diagram of an apparatus of variation amplification, according to an implementation. 
         FIG. 20  is a block diagram of an apparatus of variation amplification, according to an implementation; 
         FIG. 21  is a block diagram of an apparatus to generate and present any one of a number of biological vital signs from amplified motion, according to an implementation; 
         FIG. 22  is a block diagram of an apparatus of variation amplification, according to an implementation; 
         FIG. 23  is a block diagram of an apparatus of variation amplification, according to an implementation; 
         FIG. 24  is an apparatus that performs variation amplification to generate biological vital signs, according to an implementation; 
         FIG. 25  is a flowchart of a method of variation amplification, according to an implementation; 
         FIG. 26  is a flowchart of a method of variation amplification, according to an implementation that does not include a separate action of determining a temporal variation; 
         FIG. 27  is a flowchart of a method of variation amplification, according to an implementation; 
         FIG. 28  is a flowchart of a method of variation amplification, according to an implementation; 
         FIG. 29  is a flowchart of a method of variation amplification from which to generate and communicate biological vital signs, according to an implementation; 
         FIG. 30  is a flowchart of a method to estimate a body core temperature from an external source point in reference to a cubic relationship, according to an implementation; 
         FIG. 31  is a flowchart of a method to estimate a body core temperature from an external source point and other measurements in reference to a cubic relationship, according to an implementation; 
         FIG. 32  is a block diagram of a hand-held device, according to an implementation; 
         FIG. 33  illustrates an example of a computer environment, according to an implementation; 
         FIG. 34  is a representation of a display that is presented on the display device of apparatus in  FIGS. 3-14 and 35-39 , according to an implementation; 
         FIG. 35  is a portion of a schematic of a circuit board of a non-touch thermometer, according to an implementation; 
         FIG. 36  is a portion of the schematic of the non-touch thermometer having the digital IR sensor, according to an implementation; 
         FIG. 37  is a portion of the schematic of the non-touch thermometer having the digital IR sensor, according to an implementation; 
         FIG. 38  is a circuit that is a portion of the schematic of the non-touch thermometer having the digital IR sensor, according to an implementation; 
         FIG. 39  is a circuit that is a portion of the schematic of the non-touch thermometer having the digital IR sensor, according to an implementation; 
         FIG. 40  is a block diagram of a solid-state image transducer, according to an implementation; and 
         FIG. 41  is a block diagram of the communication subsystem, according to an implementation. 
     
    
    
     DETAILED DESCRIPTION 
     In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific implementations which may be practiced. These implementations are described in sufficient detail to enable those skilled in the art to practice the implementations, and it is to be understood that other implementations may be utilized and that logical, mechanical, electrical and other changes may be made without departing from the scope of the implementations. The following detailed description is, therefore, not to be taken in a limiting sense. 
     The detailed description is divided into ten sections. In the first section, an overview of two implementations is shown. In the second section, implementations of apparatus of digital non-touch thermometers and vital sign variation amplification detectors are described. In the third section, implementations of apparatus of non-touch cubic-estimation thermometers are described. In the fourth section, implementations of apparatus of non-touch cubic estimation thermometers and vital sign detectors are described. In the fifth section, methods of digital infrared thermometers are described. In the sixth section, implementations of apparatus of vital sign variation amplification detectors are described. In the seventh section, implementations of methods of vital sign amplification are described. In the eighth section, implementations of methods of non-touch cubic-estimation are described. In the ninth section, hardware and operating environments in which implementations may be practiced are described. Finally, in the tenth section, a conclusion of the detailed description is provided. 
       FIG. 1  is a block diagram of an overview of an electronic medical records (EMR) capture system  100 , according to an implementation. 
       FIG. 1  shows high level components of the EMR capture system  100  that includes a network server  102 . The network server  102  is also referred to as the “bridge  102 ”. The bridge  102  transfers patient measurement records (PMRs)  103  from hand-held medical-data capture-devices  104  to EMR systems in hospital and clinical environments. Each PMR  103  includes patient measurement data, such as vital sign  136  in  FIGS. 1-7 and 7-10 , estimated body temperature  412  in  FIG. 4-10 , vital sign  1416  in  FIG. 14-21 , and heartrate  1910 , respiratory rate  1916  and EKG  1928  in  FIG. 19 . Examples of hand-held medical-data capture-devices  104  include non-touch biologic detector in  FIG. 1-3 , apparatus that estimates a body core temperature  4 - 10 , apparatus of variation amplification  FIGS. 14-18 and 20-22 , mobile device  3000  and non-touch thermometer  3300 . 
     The EMR capture system  100  includes two important aspects: 
     1. A server bridge  102  to control the flow of patient measurement data from hand-held medical-data capture-devices  104  to one or more external EMR systems  105  and to manage local hand-held medical-data capture-devices  104 . 
     2. The transfer of patient measurement data in a PMR  103 , anonymous, and other patient status information to a cloud based external EMR system  105 . 
     The bridge  102  controls and manages the flow of patient measurement data to a PMR database  108  and an EMR database  110  and provides management services to hand-held medical-data capture-devices  104 . 
     The bridge  102  provides an interface to:
         A wide range of proprietary EMR systems  105 .   Location specific services, per hospital, for verification of active operator, and if necessary, patient identifications.   A cloud based data repository (EMR database  105 ) of one or more hand-held medical-data capture-devices  104 , for the purpose of storing all measurement records in an anonymous manner for analysis. A setup, management and reporting mechanism also provided.       

     The bridge  102  accepts communications from hand-held medical-data capture-devices  104  to:
         Data format conversion and transferring patient measurement records to EMR systems  105 .   Manage the firmware and configuration settings of the hand-held medical-data capture-devices  104 .   Determine current health and status of the hand-held medical-data capture-devices  104 .   Support device level protocol for communications, TCP/IP, of that supports the following core features:
           Authentication of connected device and bridge  102     Transfer of patient measurement records to Bridge  102  with acknowledgement and acceptance by the bridge  102  or EMR acceptance.   Support for dynamic update of configuration information and recovery of health and status of the hand-held medical-data capture-devices  104 .   Support for firmware update mechanism of firmware of hand-held medical-data capture-devices  104 .   
               

     The EMR capture system  100  provides high availability, 24/7/365, with 99.99% availability. 
     The EMR capture system  100  provides a scalable server system to meet operational demands in hospital operational environments for one or both of the following deployable cases: 
     1. A local network  111  at an operational site in which the bridge  102  provides all features and functions in a defined operational network  111  to manage a system of up to 10,000+ hand-held medical-data capture-devices  104 . 
     2. Remote or Cloud based site  105  in which the bridge  102  provides all services to many individual hospital or clinical sites spread over a wide geographical area, for 1,000,000+ hand-held medical-data capture-devices  104 . 
     The bridge  102  provides a central management system for the hand-held medical-data capture-devices  104  that provides at least the following functions:
         configuration management and update of the hand-held medical-data capture-devices  104     device level firmware for all of the hand-held medical-data capture-devices  104     management and reporting methods for the hand-held medical-data capture-devices  104 , covering but not limited to:
           health and status of the hand-held medical-data capture-devices  104     battery level, replacement warning of the hand-held medical-data capture-devices  104     check/calibration nearing warning of the hand-held medical-data capture-devices  104     rechecking due to rough handling or out of calibration period of the hand-held medical-data capture-devices  104     History of use, number of measurements, frequency of use etc. of the hand-held medical-data capture-devices  104     Display of current device configuration of the hand-held medical-data capture-devices  104     Date/time of last device communications with each of the hand-held medical-data capture-devices  104     
               

     The bridge  102  provides extendable features, via software updates, to allow for the addition of enhanced features without the need for additional hardware component installation at the installation site. The bridge  102  provides a device level commission mechanism and interface for the initial setup, configuration and test of hand-held medical-data capture-devices  104  on the network  111 . The bridge  102  supports medical capture devices that are not hand-held. 
     Coverage of the EMR capture system  100  in a hospital can include various locations, wards, ER rooms, offices, Dr&#39;s Offices etc. or anywhere where automatic management of patient vital sign information is required to be saved to a remote EMR system. 
     The hand-held medical-data capture-devices  104  can communicate with a third party network bridge  112  to provide access to data storage services, EMR systems, hand-held medical-data capture-devices cloud storage system etc. 
     Networking setup, configuration, performance characteristics etc. are also determined and carried out by the third party bridge  112  or another third party, for the operational environments. The hand-held medical-data capture-devices can support the network protocols for communication with the third party bridge  112  devices. 
     In some implementations, a push data model is supported by the EMR capture system  100  between the hand-held medical-data capture-devices  104  and the bridge  102  in which connection and data are initially pushed from the hand-held medical-data capture-device  104  to the bridge  102 . Once a connection has been established and the hand-held medical-data capture-devices  104  and the bridge  102 , such as an authenticated communication channel, then the roles may be reversed where the bridge  102  will control the flow of information between the hand-held medical-data capture-devices  104  and the EMR system  105 . 
     In some implementations, the hand-held medical-data capture-device  104  are connected to the EMR capture system  100  via a WIFI connection to a Wi-Fi access point  106 . In other implementations, the hand-held medical-data capture-device  104  is connected to a docking station via a wireless or physical wired connection, i.e. local isolated WIFI, Bluetooth, serial, USB, etc., in which case the docking station then acts as a local pass-through connection and connects to the bridge  102  via a LAN interface. 
     In some implementations, the portable hand-held medical-data capture-device  104  includes a battery with limited battery power and lifetime that in some implementations needs to be conserved in order to reduce the intervals at which the battery needs to be recharged. These portable hand-held medical-data capture-devices  104  support various power saving modes and as such each device is responsible for the initiation of a connection to the wireless network and the subsequent connection to the bridge  102  that meets their own specific operational requirements. It is expected that this will provide the hand-held medical-data capture-devices  104  additional control over their own power management usage and lifetime. 
     In implementations in which the hand-held medical-data capture-devices  104  attempts connection to the bridge  102 , the bridge  102  is allocated a static IP address to reduce the IP discovery burden on the hand-held medical-data capture-devices  104  and thus connect the hand-held medical-data capture-device to the bridge  102  more quickly. More specifically, the hand-held medical-data capture-devices  104  are not required to support specific discovery protocols or domain name service (DNS) in order to determine the IP address of the bridge  102 . It is therefore very important that the bridge  102  IP address is static and does not change over the operational lifetime of EMR capture system  100  on the network  111 . 
     In some implementations installation of a new hand-held medical-data capture-device  104  on the network  111  will require configuration of the hand-held medical-data capture-device  104  for the bridge  102  of IP address and other essential network configuration and security information. Commissioning of a hand-held medical-data capture-device  104  on the network  111  in some implementations is carried out from an management interface on the bridge  102 . In this way a single management tool can be used over all lifecycle phases of a hand-held medical-data capture-devices  104  on the network  111 , such as deployment, operational and decommissioning 
     In some implementations the initial network configuration of the hand-held medical-data capture-device  104  does not require the hand-held medical-data capture-device  104  to support any automated network level configuration protocols, WPS, Zeroconfi etc. Rather the bridge  102  supports a dual network configuration, one for operational use on the operational network of the hospital or clinic, or other location, and an isolated local network, with local DHCP server, for out of the box commissioning of a new hand-held medical-data capture-device  104  and for diagnostic test of the hand-held medical-data capture-devices  104 . Hand-held medical-data capture-devices  104  can be factory configured for known network settings and contain a default server IP address on the commissioning network  111 . In addition the hand-held medical-data capture-devices  104  are required to support a protocol based command to reset the hand-held medical-data capture-device  104  to network factory defaults for test purposes 
     It is commonplace that the firmware revision of the hand-held medical-data capture-devices  104  will not be consistent in the operational environment. Therefore the bridge  102  is backwards compatible with all released firmware revisions from firmware and protocol revision, data content and device settings view point of the hand-held medical-data capture-device  104 . As a result, different revision levels of hand-held medical-data capture-devices  104  can be supported at the same time on the network  111  by the bridge  102  for all operations. 
     Implementation Alternatives 
     Limited Operational Features and Implementation Capability 
     Some implementations of the EMR capture system  100  have limited operational features and implementation capability. A significant function of the EMR capture system  100  with the limited operational features and implementation capability in the bridge  102  is to accept data from a hand-held medical-data capture-device  104  and update the EMR capture system  100 . 
     The following limited feature set in some implementations be supported by the EMR capture system  100  for the demonstrations:
         Implementation to a local IT network on a server of the local IT network, OR located on a Cloud-based PMR storage system  105 , whichever is achievable in the time frame and meets the operational requirements for third party EMR capture systems.   Acceptance of patient medical records from a hand-held medical-data capture-device  104 :
           Date and Time   Operator ID   Patient Id   Patient measurement   Device manufacturer, model number and firmware revision   
           Acceptance of limited status information from a hand-held medical-data capture-device  104 :
           Battery Level   Hospital reference   location reference   Manufacturer identification, serial number and firmware revision   Unique id number   
           Transfer of patient records from a hand-held medical-data capture-device  104 , multiple hand-held medical-data capture-devices  104  up to 10, to a third party EMR capture system and to the EMR capture system  100 , respectively in that order.   Limited user interface for status review of known hand-held medical-data capture-device  104 .   Configuration update control for detected devices providing configuration of:
           Hospital reference   Unit location reference
 
Extended Operational Features and Implementation Capability
   
               

     The following extended features are supported in extended operational features and implementation capability:
         1. A Patient Record Information and measurement display interface for use without an EMR capture system  100 .   2. Update of device firmware over the wireless network.
 
Operational Use
 
Local Network Based—Single Client
       

     In some implementations, the hand-held medical-data capture-device  104  is deployed to a local hospital, or other location, wireless IT network that supports WI-FI enabled devices. The hand-held medical-data capture-device  104  supports all local network policy&#39;s including any local security policy/protocols, such as WEP, WPA, WPA2, WPA-EPA as part of the connection process for joining the network. In some implementations, the hand-held medical-data capture-device  104  operates on both physical and virtual wireless LAN&#39;s, WAN&#39;s, and the hand-held medical-data capture-device  104  is configured for operation on a specific segment of the network. Depending on the IT network structure, when the hand-held medical-data capture-device  104  is configured for operation on a specific segment of the network, the hand-held medical-data capture-devices  104  network connection ability is limited to the areas of the operational environment for which it as be configured. Therefore, the hand-held medical-data capture-device  104  in network environments that have different network configurations is configured to ensure that when the hand-held medical-data capture-device  104  is used in various locations throughout the environment that the hand-held medical-data capture-device  104  has access in all required areas. 
     In some implementations the bridge  102  system is located on the same IT network and deployed in accordance with all local IT requirements and policy&#39;s and that the hand-held medical-data capture-devices  104  on this network are able to determine a routable path to the bridge  102 . The hand-held medical-data capture-devices  104  and the server are not required to implement any network level discovery protocols and therefore the bridge  102  is required to be allocated static IP address on the network. In the case where a secondary bridge  102  device is deployed to the network as a backup for the primary, or the bridge  102  supports a dual networking interface capability, then the secondary bridge  102  IP address is also required to be allocated a static IP address. It is important that the static IP primary and secondary address, if supported, remain constant to ensure proper and continuous system operation. When the IP address of the bridge  102  is changed then all devices configured with the old IP address are then unable to find the bridge  102  device on the network and the hand-held medical-data capture-devices  104  is manually reconfigured for operation. 
     A benefit of this bridge  102  implementation to the local IT network infrastructure is the reduction in latency times for data sent between the hand-held medical-data capture-devices  104  and the bridge  102 . 
     It is important to note that this is a single organization implementation and as such the bridge  102  is configured to meet the security and access requirements of a single organization. 
       FIG. 2  is a block diagram of an overview of an electronic medical records (EMR) capture system  200  having a remote cloud based bridge  102 , according to an implementation. 
     An implementation of a remote cloud-based bridge  102  for a single client is similar to the local network case described at the end of the description of  FIG. 1 , with the exception that the bridge  102  is not physically located at the physical site of the hand-held medical-data capture-devices  104 . The site, such as a hospital, has deployed the bridge  102  to a remote site, their specific IT center or on a Cloud-based PMR storage system  105 . 
     The physical locale of the bridge  102  is transparent to the hand-held medical-data capture-device  104 . However in some implementations data latency between the bridge  102  and the hand-held medical-data capture-devices  104  on the hand-held medical-data capture-device  104  is too large to provide a positive user experience. 
     Again as in the local install case, the same user access and security policies are in place for the single operating organization. 
     Remote Based—Multiple Client Support 
     In some implementations for smaller organizations or for organizations that do not have a supporting IT infrastructure or capability that a remote bridge  102  system is deployed to support more than one organization. Where the remote bridge  102  system is deployed to support more than one organization, the bridge  102 , or servers, can be hosted as a cloud based system. In this case the hand-held medical-data capture-devices  104  are located at the operational site for the supported different geographical location organizations and tied to the bridge  102  via standard networking methods via either private or public infrastructure, or a combination thereof. 
     Where a remote, i.e. non-local IT network, system is deployed to support more than one hospital or other organization EMR capture system  100  includes components that isolate each of the supported organizations security and user access policy&#39;s and methods along with isolating all data transfers and supporting each organizations data privacy requirements. In addition system performance is required to be balanced evenly across all organizations. In this case each organization can require their specific EMR capture system  100  be used, their EMR capture system  100  will have to be concurrently operational with many diverse EMR capture systems  100 . 
     Single Measurement Update 
     The primary function of the hand-held medical-data capture-device  104  is to take a patient temperature, display the result to the operator and to save the patient information and temperature to an EMR capture system  100  via the bridge  102 . 
     Normally the hand-held medical-data capture-device  104  is in a low power state simply waiting for an operator to activate the unit for a patient measurement. Once activated by the operator EMR capture system  100  will power up and under normal operating conditions guide the operator through the process of patient temperature measurement and transmission of the patient record to the bridge  102  for saving to the EMR capture system  100 . 
     Confirmation at each stage of the process to the operator is required, i.e. data is valid, to ensure a valid identified patient result is obtained and saved to the EMR, the key confirmation point is: Saving of data to EMR/Bridge  102   
     In some implementations, the confirmation at each stage in some implementations is provided by either the bridge  102  device or the EMR capture system  100 . 
     When confirmation is provided by the bridge  102  it is an acknowledgment to the hand-held medical-data capture-device  104  that the bridge  102  has accepted the information for transfer to the EMR capture system  100 (s) in a timely manner and is now responsible for the correct management and transfer of that data. 
     When confirmation is provided by the EMR capture system(s)  100 , the bridge  102  is the mechanism via which the confirmation is returned to the hand-held medical-data capture-device  104 . That is the hand-held medical-data capture-device  104  sends the data to the bridge  102  and then waits for the bridge  102  to send the data to the EMR and for the EMR to respond to the bridge  102  and then the bridge  102  to the hand-held medical-data capture-device  104 , 
     In some implementations depending on the operational network and where the bridge  102  is physically located, i.e. local or remote, that the type of confirmation is configurable. For a remote located bridge  102  the latency time involved in the EMR level confirmation can be deem too long for an acceptable user experience. 
     In the event that the hand-held medical-data capture-device  104  cannot join the network or the bridge  102  device cannot be communicated with or the bridge  102  or EMR capture system  100 ( s ) level confirmation is not received the hand-held medical-data capture-device  104  will maintain an internal non-volatile storage mechanism for unsaved patient records. It is not acceptable for the hand-held medical-data capture-device  104  to simply not provide its primary clinical purpose in light of these possible operational issue. If the hand-held medical-data capture-device  104  has saved records present in its internal memory then the hand-held medical-data capture-device  104  will in a timely automatic manner attempt to transfer the saved records to the bridge  102  for processing. 
     Heartbeat 
     The hand-held medical-data capture-devices  104  in order to obtain date/time, configuration setting, provide status information to the bridge  102 , transfer saved patient records and check for a firmware update will provide a mechanism to on a configured interval automatically power up and communicate to the configured bridge  102  without operator intervention. 
     Accordingly the and outside of the normal clinical use activation for the hand-held medical-data capture-device  104 , the hand-held medical-data capture-device  104  can both update its internal settings, and provide status information to the bridge  102  system. 
     If these actions where left to only the operator startup case of the hand-held medical-data capture-device  104  for operational clinical use then there is an unacceptable delay to the operator in proceeding to the measurement action of the hand-held medical-data capture-device  104 . This is deemed acceptable and the hand-held medical-data capture-device  104  in some implementations make best efforts to maintain its operational status independent of the clinical use case. 
     Automatic Transfer of Saved Patient Measurement Records (PMRs) 
     If the hand-held medical-data capture-device  104  for an unknown reason has been unable to either join the network or connect to the bridge  102  or receive a bridge  102  or EMR capture system  100 (s) level acknowledge that data has been saved the hand-held medical-data capture-device  104  will still allow the primary clinical temperature measurement function to be carried out and will save the resultant PMR in non-volatile internal memory up to a supported, configured, maximum number of saved patient records on the hand-held medical-data capture-device  104 . 
     When the hand-held medical-data capture-device  104  is started for a measurement action the hand-held medical-data capture-device  104  will determine if it contains any saved patient records in its internal memory. If one or more saved patient records are detected then the hand-held medical-data capture-device  104  will attempt to join the network immediately, connect to the bridge  102  and send the patient records one at a time to the bridge  102  device while waiting for the required confirmation that the bridge  102  has accepted the patient record. Note in this case confirmation from the EMR capture system  100  is not required. On receipt of the required validation response from the remote system the hand-held medical-data capture-device  104  will delete the patient record from its internal memory. Any saved patient record that is not confirmed as being accepted by the remote device is maintained in the hand-held medical-data capture-devices  104  internal memory for a transfer attempt on the next power up of the hand-held medical-data capture-device  104   
     The hand-held medical-data capture-device  104  on the heart beat interval all also carry out this function. In some implementations the hand-held medical-data capture-device  104  will reduce its heart beat interval when saved patient records are present on the hand-held medical-data capture-device  104  in order to ensure that the records are transferred to the bridge  102 , EMR, in a timely manner once the issue has been resolved. When this transfer mechanism is active status information is presented to the operator on the hand-held medical-data capture-device  104  screen. 
     Under this operation it is possible for the bridge  102  device to receive from a single hand-held medical-data capture-device  104  multiple patient record transfer requests in rapid sequence. 
     Device Configuration 
     The hand-held medical-data capture-device  104  on connection to the bridge  102 , heart beat interval or operator activated, will provide the bridge  102  with its model number and all appropriate revisions numbers and unique identification to allow the bridge  102  to determine the hand-held medical-data capture-device  104  capabilities and specific configurations for that device. 
     The hand-held medical-data capture-device  104  will query the bridge  102  for its device parameters and if different from the hand-held medical-data capture-devices  104  current setting update the hand-held medical-data capture-devices  104  setting to the new setting value as provided by the bridge  102 . 
     Accordingly, in some implementations the bridge  102  will act as the central repository for device configuration, either for a single device, a group of defined devices or an entire model range. 
     Device Date/Time Update 
     In implementations where there is no mechanism on the hand-held medical-data capture-device(s)  104  for the user to configure date and time on the hand-held medical-data capture-device  104  via its user interface. 
     All embedded systems with a real time clock function, RTC, will drift with time due to the accuracy of their specific RTC hardware, ambient and operational temperature. 
     It is therefore expected that each device on connection to the bridge  102  will query the bridge  102  for the current date and time and update the hand-held medical-data capture-devices  104  internal RTC clock based on the provided information. 
     The hand-held medical-data capture-devices  104  will query the bridge  102  on the defined heart beat interval or when they are started by the operator upon joining the network. 
     The bridge  102  is therefore expected to support an accurate date and time mechanism, with leap year capability, as per the local IT policy. If no local IT policy is in place then the bridge  102  is to maintain date and time against a known accurate source, e.g. a web based time server. 
     Accordingly, in some implementations all devices is maintained at the same date and time across the operation of EMR capture system  100  and the capabilities of the hand-held medical-data capture-devices  104 . 
     Device Status Management 
     In some implementations the bridge  102  provides a level of device management for the hand-held medical-data capture-devices  104  being used with EMR capture system  100 . In some implementations, the bridge  102  is able to report and determine at least the following:
         Group and sort devices by manufacture, device model, revisions information and display devices serial numbers, unique device ID, asset number, revisions, etc. and any other localized identification information configured into the hand-held medical-data capture-device  104 , e.g. ward location reference or Hospital reference   The last time a specific unit connected to EMR capture system  100     The current status of the a given device, battery level, last error, last date of re-calibration of check, or any other health indicator supported by the hand-held medical-data capture-device  104 .   Report devices out of their calibration period, or approaching their calibration check   Report devices that require their internal battery replaced   Report devices that require re-checking due to a detected device failure or error condition, or that have been treat in a harsh manner or dropped.   Determine if a hand-held medical-data capture-device  104  has not connected for a period of time and identify the hand-held medical-data capture-device  104  as lost or stolen. If the hand-held medical-data capture-device  104  reconnects to the network after this period of time then the hand-held medical-data capture-device  104  in some implementations is highlighted as requiring an accuracy check to ensure that it is operational. Note the hand-held medical-data capture-devices  104  will likely also support this capability and after a pre-determined time disconnected from the network inhibit their measurement function until a hand-held medical-data capture-device  104  level recheck is carried out   Provide a mechanism to commission and decommission devices onto and off of the network. If a hand-held medical-data capture-device  104  has not been specifically commissioned for operation on the network then it in some implementations is not be allowed to access the core services supported by the bridge  102  even if it has configured for operation on the EMR capture system  100 
 
Firmware Update
       

     In some implementations a firmware update for a given device model is scheduled on the network as opposed to simply occurring. It is considered unacceptable if a hand-held medical-data capture-device  104  is activated for a patient measurement and then carries out a firmware update, this delays the patient vital sign measurement. 
     Instead the bridge  102  system will support a firmware update roll out mechanism where the date and time of the update can be scheduled and the number of devices being updated concurrently can be controlled. 
     In some implementations, when a hand-held medical-data capture-device  104  connects to the bridge  102  due to a heartbeat event that the hand-held medical-data capture-device  104  will query the bridge  102  to determine if the firmware update for that model of device is available and verify if its firmware, via revision number, is required to be updated. The bridge  102  will respond to the hand-held medical-data capture-device  104  based on if a firmware update is available and the defined schedule for the update process. 
     If there is an update available but the current time and date is not valid for the schedule then the bridge  102  will information the hand-held medical-data capture-device  104  that there is an update but that the update process is delayed and update the hand-held medical-data capture-devices  104  firmware check interval configuration. The firmware check interval setting will then be used by the hand-held medical-data capture-device  104  to reconnect to the bridge  102  on a faster interval than the heartbeat interval in order to facilitate a more rapid update. For e.g. the firmware update schedule on the bridge  102  in some implementations is for every night between 2 pm and 4 pm, the interval timer in some implementations will then be set to for example, every 15 minutes as opposed to a heartbeat time interval of, for example, every 24 hours. 
     The hand-held medical-data capture-device  104  if the firmware check interval is non-zero will then on the firmware check interval connect to the bridge  102  and retest for the firmware update to be carried out. Once the hand-held medical-data capture-device  104  has connected to the bridge  102  for the updated and the schedule date and time are valid, along with the concurrent number of devices being updated, the hand-held medical-data capture-devices  104  firmware update procedure is followed, the hand-held medical-data capture-devices  104  firmware updated and EMR capture system  100  verified for normal operational use. As part of the firmware update procedure the hand-held medical-data capture-devices  104  firmware check internal is reset back to 0 or done so by the hand-held medical-data capture-device  104  on the next active condition to the bridge  102 . 
     In some implementations the bridge  102  will manage the firmware update process for many different hand-held medical-data capture-devices  104  each with their specific update procedure, update file formats, and verification methods and from a date and time scheduling mechanism and the number of devices being update concurrently. In addition in some implementations the bridge  102  will provide a mechanism to manage and validate the firmware update files maintain on the bridge  102  for use with the hand-held medical-data capture-devices  104 . 
     This section concludes with short notes below on a number of different aspect of the EMR data capture system  100  and  200  follow on numerous topics: 
     Remote—single client: The bridge  102  architecture and implementation in some implementations cater for remote operation on a hospital network system. Remote operation is seen as external to the network infrastructure that the hand-held medical-data capture-devices  104  are operational on but considered to be still on the organizations network architecture. This can be the case where a multiple hospital-single organization group has deployed EMR capture system  100  but one bridge  102  device services all hospital locations and the bridge  102  is located at one of the hospital sites or their IT center. 
     Remote—multiple client support: The bridge  102  architecture and implementation in some implementations is limited to remote operation on a cloud based server that supports full functionality for more than one individual separate client concurrently. Where we deploy a cloud based single or multiple server system to service 1 or more individual hospital/clinical organization. The IT system and security requirements for each client in some implementations be meet in this case, different organizations will have different interface requirements. 
     Multiple concurrent EMR support: For a single remote bridge  102  servicing multiple clients EMR capture system  100  supports interfacing to an independent EMR, and different EMR vendor, concurrently for each support client. With one bridge  102  servicing multiple clients each client can/will require the patient measurements to be sent to a different EMR capture system  100 . These EMR capture system(s)  100  will in all likelihood be provided by different vendors. 
     Single organization device support: The bridge  102  supports at least 10000+ hand-held medical-data capture-devices  104  for a single client for either local or remote implementation. Note the supported hand-held medical-data capture-devices  104  may be from diverse hand-held medical-data capture-device  104  manufacturers. 
     Support Different EMR for same client: The bridge  102  architecture for operation in a single client organization supports the user by the organization of different EMR capture system(s)  100  from different departments of wards in the operational environment. It is not uncommon for a single organization to support multiple different EMR capture system(s)  100  for different operational environments, for example, Cardiology and ER. EMR capture system  100  in some implementations takes this into account and routes the patient data to the correct EMR capture system  100 . Therefore the bridge  102  is informed for a given hand-held medical-data capture-device  104  which indicates to the EMR the medical data has to be routed to. 
     Segregation of operations for multiple client operations on single bridge  102 : 
     EMR capture system  100  supports per client interfaces and functionality to ensure that each client&#39;s configurations, performance, user accounts, security, privacy and data protection are maintained. For single server implementations that service multiple independent hospital groups the bridge  102  in some implementations maintain all functionality, and performance per client separately and ensure that separate user accounts, bridge  102  configuration, device operation, patient and non-patient data, ERM interfaces etc. are handled and isolated per client. A multiple cloud based implementation in this case will obviate this function as each client includes their own cloud based system, but this is at a higher cost. 
     Multiple organization device support: The bridge  102  supports at least 1 million+ hand-held medical-data capture-devices  104  for remote implementations that services multiple separate hospital systems. The supported hand-held medical-data capture-devices  104  can be from different hand-held medical-data capture-device  104  manufacturers. 
     EMR capture system support: The hand-held medical-data capture-device  104  supports a wide range of EMR capture system(s)  100  and is capable of interfacing to any commercially deployed EMR capture system  100 . 
     EMR capture system interface and approvals: The bridge  102  device provides support for all required communication, encryption, security protocols and data formats to support the transfer of PMR information in accordance with all required operational, standards and approval bodies for EMR capture system(s)  100  supported by the EMR capture system  100 . 
     Remote EMR capture system(s): The bridge  102  supports interfacing to the required EMRs systems independent of the EMR capture system(s)  100  location, either locally on the same network infrastructure or external to the network that the bridge  102  is resided on or a combination of both. The EMR capture system  100 , or systems, that the bridge  102  is required to interact with and save the patient to can not be located on the same network or bridge  102  implementation location, therefore the bridge  102  implementation in some implementations ensure that the route to the EMR exists, and is reliable. 
     Bridge buffering of device patient records: The bridge  102  device provides a mechanism to buffer received PMRs from connected hand-held medical-data capture-devices  104  in the event of a communications failure to the EMR capture system  100 , and when communications has been reestablished subsequently transfer the buffered measurement records to the EMR. It is expected from time to time in normal operation that the network connection from the bridge  102  to the configured EMR capture system  100  is lost. If communications has been lost to the configured EMR capture system(s)  100  then the bridge  102  in some implementations accepts measurement records from the hand-held medical-data capture-devices  104  and buffers the measurement records until communications has be reestablished. Buffering the measurement records allows the medical facility to transfer the current data of the medical facility to the bridge  102  for secure subsequent processing. In this event the bridge  102  will respond to the hand-held medical-data capture-device  104  that either 1. Dynamic validation of EMR acceptance is not possible, or 2. The bridge  102  has accepted the data correctly. 
     Bridge  102  real time acknowledge of EMR save to device: The bridge  102  provides a mechanism to pass to the hand-held medical-data capture-device  104  confirmation that the EMR has accepted and saved the PMR. The bridge  102  when configured to provide the hand-held medical-data capture-device  104  with real time confirmation that the EMR capture system  100 ( s ) have accepted and validated the PMR. This is a configuration option supported by the bridge  102 . 
     Bridge  102  real time acknowledgement of acceptance of device PMR: The bridge  102  provides a mechanism to pass to the hand-held medical-data capture-device  104  confirmation that the bridge  102  has accepted the PMR for subsequent processing to the EMR. The hand-held medical-data capture-device  104  in some implementations verifies that the bridge  102  has accepted the PMR and informs the operator of the hand-held medical-data capture-device  104  that the data is secure. This level of confirmation to the hand-held medical-data capture-device  104  is considered the minimum level acceptable for use by the EMR capture system  100 . Real time acknowledgement by the bridge  102  of acceptance of the PMR from the device is a configuration option supported by the bridge  102 . 
     Bridge Date and Time: The bridge  102  maintains internal date and time against the local network time source or a source recommended by the IT staff for the network. All transitions and logging events in some implementations are time stamped in the logs of the bridge  102 . The hand-held medical-data capture-device  104  will query the bridge  102  for the current date and time to update its internal RTC. The internal time of hand-held medical-data capture-device  104  can be maintained to a +/−1 second accuracy level, although there is no requirement to maintain time on the hand-held medical-data capture-device  104  to sub one-second intervals. 
     Graphical User Interface: The bridge  102  device provides a graphical user interface to present system information to the operator, or operators of EMR capture system  100 . The user interface presented to the user for interaction with EMR capture system  100  in some implementations be graphical in nature and use modern user interface practices, controls and methods that are common use on other systems of this type. Command line or shell interfaces are not acceptable for operator use though can be provided for use by system admin staff. 
     Logging and log management: The bridge  102  is required to provide a logging capability that logs all actions carried out on the bridge  102  and provides a user interface to manage the logging information. Standard logging facilities are acceptable for this function for all server and user actions. Advanced logging of all device communications and data transfers in some implementations is also provided, that can be enabled/disable per hand-held medical-data capture-device or for product range of hand-held medical-data capture-devices. 
     User Accounts: The bridge  102  device provides a mechanism to support user accounts on the hand-held medical-data capture-device  104  for access control purposes. Standard methods for user access control are acceptable that complies with the operational requirements for the install/implementation site. 
     User Access Control: The bridge  102  device supports multiple user access control that defines the access control privileges for each type of user. Multiple accounts of each supported account type are to be support. Access to EMR capture system  100  in some implementations be controlled at a functional level, In some implementations, the following levels of access is provided:
         System Admin: provides access to all features and functions of EMR capture system  100 , server and device based.   Device Admin: provides access only to all device related features and functions supported by the EMR capture system  100 .   Device Operator: provides access only to device commissioning, and configuration.   Device Installer: provides access only to device commissioning and test capabilities.   A user account can be configured for permissions for one or more account types.       

     Multi-User Support: The bridge  102  device is required to provide concurrent multi-user support for access and management of the bridge  102  system across all functions. Providing multiple user access is deemed a necessary operational feature to support. 
     Modify User Accounts: The bridge  102  provides a method to create, delete, and edit the supported user accounts and supported access privileges per account. 
     Bridge Data Corruption/Recovery: The bridge  102  architecture and implementation in some implementations ensure that under an catastrophic failure of EMR capture system  100  or a storage component that no data is lost that has not been confirmed as saved to the either the EMR for PMRs or localize storage for operational data pertaining to the non-patient data maintained by the EMR capture system  100 . The bridge  102  supports a method to ensure zero data lost under critical and catastrophic system failure of the bridge  102  or any of the bridge  102  components, network interfaces, storage systems, memory contents, etc. for any data handled by the EMR capture system  100 . In the event of a recovery action where a catastrophic failure has occurred EMR capture system  100  supports both the recovery action and its normal operational activities to ensure that EMR capture system  100  is active for clinical use. 
     Bridge availability: The bridge  102  device is a high availably system for fail safe operation 24/7/365, with 99.99% availability, i.e. “four nines” system. The bridge  102  implementation is expected to meet an availability metric of 99.99%, i.e. a “four nines” system because the bridge  102  hardware in some implementations is implemented with a redundant dual server configuration to handle single fault conditions. The bridge  102  is required to have an independent power source or if the installation site has a policy for power loss operation the bridge  102  installation in some implementations comply with the policy requirements. 
     Bridge  102  Static IP address and port Number: The bridge  102  provides a mechanism to configure the bridge  102  for a primary use static IP address and port number For hand-held medical-data capture-device  104  connection to the bridge  102 , the bridge  102  in some implementations have a static IP address and that IP address in some implementations be known by the hand-held medical-data capture-device  104 . Bridge  102  Dual network capability: The bridge  102  system provides a mechanism to support a dual operational network interface to allow for failure of the primary network interface. This secondary network interface is required to support a configurable static IP address and port number A redundant network connection in some implementations be provided to cover the event that the primary network interface has failed. Note if the bridge  102  implementation for EMR capture system  100  employs two separate bridges  102  or other redundant mechanism to provide a backup system then this requirement can be relaxed from an operational view point, however EMR capture system  100  in some implementations support this mechanism. 
     Local WIFI commissioning network: The bridge  102  provides a mechanism on the local operational network to commission new hand-held medical-data capture-devices  104  for operational use. EMR capture system  100  is to supply a localized isolated network for the use of commissioning new devices onto the operational network. The bridge  102  is to have a known default IP address on this network and provide a DHCP server for the allocation of IP address to devices on EMR capture system  100 . The commissioning of new devices is to be considered a core aspect of the bridge  102  functions. However it is acceptable that a separate non server based application in some implementations will manage the configuration process provided the same user interface is presented to the user and the same device level configuration options are provided. In some implementations, the configuration of a new hand-held medical-data capture-device  104  on the network is carried out in two stages: 1. Stage 1: network configuration from the commissioning network to the operational network 2. Stage 2: Once joined on the operational network specific configuration of the hand-held medical-data capture-device  104  for clinical/system function operation. 
     Remote commissioning of devices: EMR capture system  100  provides a mechanism where the bridge  102  device is not present on the local network for a new device is to be commissioned on the operational network. Even when the bridge  102  is on a cloud server external to the operational site network new devices in some implementations be commissioned onto the network in the same manner as if the bridge  102  was a local server. This does not preclude the installation of a commission relay server on to the operational network that supports this mechanism. 
     Device setup: The bridge  102  supports the configuration of a device level network operation and security settings for an existing or new hand-held medical-data capture-device  104  on either the commissioning network or the operational network. New devices are configured on the commissioning network. Existing devices on the operational network are also configurable for network and security requirements. Independent of the network that the hand-held medical-data capture-device  104  is currently connected to, the bridge  102  provides the required user interface for the configuration of the network operational and security settings by the operator. Once configured, a method of verifying that the hand-held medical-data capture-device  104  has been configured correctly is presented to the operator to prove that the hand-held medical-data capture-device  104  is operational. Devices support a network command to reboot and rejoin the network for this verification purpose. 
     Bridge Configuration: The bridge provides a mechanism to support configuration of all required bridge  102  specific control options. A method to configure the bridge  102  functions in some implementations is provided for all features where a configuration option enable, disable or a range of parameters are required. 
     Bridge Hand-held medical-data capture-device acknowledgement method: The bridge  102  provides a configuration method to control the type of ACK required to be supported by the EMR capture system  100 , one of: •Device configuration dependent, •EMR level ack •Bridge  102  level ack. In some implementations, A hand-held medical-data capture-device  104  requires from the bridge  102  an acknowledgement that the PMR has been saved by the EMR capture system  100  or accepted for processing by the bridge  102 . 
     EMR Level: Bridge  102  confirms save by EMR capture system  100 . 
     Bridge Level: bridge  102  controlled, accepted for processing by the bridge  102 . 
     Enabled/Disable of firmware updated mechanism: The bridge  102  provides a method to globally enable or disable the supported hand-held medical-data capture-device  104  firmware updated feature. A global enable/disable allows the control of the firmware update process. 
     Server Management: The bridge  102  is required to provide a user interface that provides configuration and performance monitoring of the bridge  102  and platform functions. 
     System Reporting: The bridge  102  provides a mechanism to provide standard reports to the operator on all capabilities of the bridge  102  system. Standard reporting in some implementations includes selection of report parameters, sorting of report parameters, printing of reports, export of reports to known formats, WORD, excel, PDF etc., identification of reports, organization name, location, page numbers, name of report etc., date and time of log, generate by user type and extent of, provides full reporting for all system features and logs. Examples are: list of devices known to EMR capture system  100 , with location reference and date and time of last connection, report on the battery status for all known hand-held medical-data capture-devices  104 , report on any devices that reported an error, report on devices that have expired calibration dates, and report on devices that are approaching calibration dates. 
     Demo Patient Interface: The bridge  102  provides a mechanism for demo only purposed where an EMR capture system  100  is not available for interfacing to EMR capture system  100  to allow patient records received from a given device to be viewed and the vital sign data presented. For demonstrations of EMR capture system  100  where there is no EMR capture system  100  to connect the bridge  102  system to provides a user interface method to present the data sent to the bridge  102  by the connected hand-held medical-data capture-devices  104 . In some implementations, this patient data interface manages and stores multiple patients and multiple record readings per patient and present the information to the operator in an understandable and consistent manner. 
     Interface to PMR database: The bridge  102  device provides an interface to the PMR database  108  system for the purpose of storing anonymous patient records and device specific status information. Anonymous PMRs are stored for the purposes of data analysis as well as provide a mechanism to monitor the operation of the hand-held medical-data capture-devices  104 . 
     Device PMRs: The bridge  102  in some implementations accepts propriety formatted measurement records from hand-held medical-data capture-devices  104  connected and configured to communicate with the bridge  102  and translate the received measurement record into a suitable format for transfer to a EMR capture system  100 . The bridge  102  is the hand-held medical-data capture-device  104  that will take the hand-held medical-data capture-device  104  based data and translate that data into a format suitable to pass along to a local or remote EMR system using the required protocols of that EMR capture system  100 . 
     Device non patient measurement data: The bridge  102  in some implementations accept meta data from connected hand-held medical-data capture-devices  104  and provide meta data to a connected device. Meta data is any other data or setting parameter associated with the hand-held medical-data capture-device  104  that in some implementations is managed by the bridge  102 , e.g. device configuration settings, firmware images, status information etc. 
     Device to Bridge  102  interface protocol: The bridge  102  supports a hand-held medical-data capture-device  104  to bridge  102  interface protocol, BRIP, for all communications between the hand-held medical-data capture-devices  104  and the bridge  102  device. Each device supports a single interface protocol, BRIF, but it in some implementations this can be impracticable and individual device or manufacture level protocols can have to be supported by the bridge  102 , the bridge  102  architecture is therefore required to take this into account. 
     Network communications method: The bridge  102  is required to support a LAN based interface for processing connection requests and data transfers from remote hand-held medical-data capture-device  104 . Standard communications methods such as UDP/TCP/HTTP etc. are supported, including TCP/IP sockets, but the interface is not restricted to this transfer mechanism, the architecture of EMR capture system  100  in some implementations support other transfer methods such as UDP, HTTP, web servers. Where more than one hand-held medical-data capture-device  104  type is supported in EMR capture system  100  the bridge  102  is required to support different transfer mechanism concurrently Hand-held medical-data capture-devices  104 : The bridge  102  in some implementations accept connections and measurement data records from hand-held medical-data capture-device(s)  104 . 
     Thermometer devices: The first hand-held medical-data capture-devices  104  to be supported by the EMR capture system  100  are to hand-held medical-data capture-device(s)  104 . 
     Non-conforming Hand-held medical-data capture-devices: The bridge  102  in some implementations accepts connections and measurement data records from non-hand-held medical-data capture-device(s)  104  hand-held medical-data capture-devices  104  using device interface protocols specific to a given device or manufacture of a range of device. The EMR capture system  100  supports third party hand-held medical-data capture-devices  104  to provide the same core features and functions as those outlined in this document. In some implementations, a core system supports all hand-held medical-data capture-devices  104  connected to EMR capture system  100 , for the purposes of measurement data, temperature, ECG, blood pressure, plus other vital signs, both single and continuous measurement based, for transfer to the selected EMR capture system  100 , along with per device configuration and status monitoring. 
     Single Parameter Measurement Data: The bridge  102  in some implementations accept and processes for transfer to the configured EMR capture system  100 , single event measurement data. Single event measurement data is defined as a patient vital sign single point measurement such as a patient temperature, blood pressure, heart rate or other data that is considered a one-time measurement event for a single measurement parameter. This type of data is generated from a hand-held medical-data capture-device  104  that supports a single vital sign. 
     Multiple Parameter Measurement Data: The bridge  102  in some implementations accept and process for transfer to the EMR multiple event measurement data. Multiple event measurement data is defined as a patient vital sign single point measurement such as a patient temperature, blood pressure, heart rate or other parameter that is considered a one-time measurement event for more than one parameter. This type of data is generated from a multi-vital sign hand-held medical-data capture-device  104 . 
     Continuous Parameter Measurement Data: The bridge  102  in some implementations accept and process for transfer to the EMR single parameter continuous measurement data. Continuous measurement data is defined as a stream of measurement samples representing a time domain signal for a single vital sign parameter. 
     Unique Hand-held medical-data capture-device ID: The bridge  102  supports a unique identifier per hand-held medical-data capture-device  104 , across all vendors and device types, for the purposes of device identification, reporting and operation. Each hand-held medical-data capture-device  104  that is supported by the EMR capture system  100  provides a unique ID based on the manufacture, product type, and serial number or other factors such as the FDA UID. The bridge  102  is required to track, take account of, and report this number in all interactions with the hand-held medical-data capture-device  104  and for logging. This device ID can also be used in the authentication process when a hand-held medical-data capture-device  104  connects to the bridge  102 . 
     Device connection authentication: The bridge  102  provides a mechanism to authenticate a given hand-held medical-data capture-device  104  on connection to ensure that the hand-held medical-data capture-device  104  is known and allowed to transfer information to the bridge  102 . Access to the bridge  102  functions in some implementations be controlled in order to restrict access to currently allowed devices only. Acceptance of a hand-held medical-data capture-device  104  making connection the bridge  102  for 2 main rationales. 1. the hand-held medical-data capture-device  104  is known to the bridge  102 , and that 2. a management function to control access for a given device, i.e. allow or bar access. 
     Device date and time update: The bridge  102  device can provide a mechanism to allow a connected hand-held medical-data capture-device  104  to update its internal date and time settings against the bridge  102 &#39;s current date and time. The hand-held medical-data capture-devices  104  are to update their internal real time clocks on connection to the bridge  102 , accordingly, a time reference across all devices used with EMR capture system  100  is obtained from a central source. All embedded systems real time clock functions drift with time and operational and ambient temperature, this mechanism will form the basis of both time and date configuration on the hand-held medical-data capture-device  104  and dynamic update of time and date for the hand-held medical-data capture-device  104  thereby removing the need to set time and date on a given device. An accuracy of +/−1 second is acceptable for maintaining the time on a hand-held medical-data capture-device  104 . Bridge  102  to device backwards compatibility: The bridge  102  device is required to be backwards compatible with all released versions of hand-held medical-data capture-device  104  firmware, interface protocols, and data formats supported by the bridge  102  device from first release of the bridge  102  system. Backwards compatibly of the bridge  102  with all released revisions of hand-held medical-data capture-device  104  is a in some implementations for the normal operation of EMR capture system  100 . It cannot be guarantee that all devices of a given product are at the same revision level or that different products from a single manufacture or from different manufactures will support the same interface protocol or other critical component revision. 
     Last connection of device: The bridge  102  is required maintain a history of the connection dates and times for a given hand-held medical-data capture-device  104 . This is required from a reporting and logging viewpoint. In some implementations will also be used to determine if a hand-held medical-data capture-device  104  is lost/stolen or failed. 
     Calibration/Checker Monitoring: The bridge  102  is required to track the valid calibration dates for a given device and present to the operator does devices that are out of calibration or approaching calibration All hand-held medical-data capture-devices  104  in some implementations be checked for operation and accuracy on a regular bases. EMR capture system  100  can provide the facility to generate a report and high light devices that are either out of calibration and those approaching calibration. The check carried out by the bridge  102  is on the expiry date exposed by the hand-held medical-data capture-device  104 . The bridge  102  is not required to actually check the hand-held medical-data capture-device  104  for calibration, only report if the hand-held medical-data capture-device  104  is out of calibration based on the hand-held medical-data capture-devices  104  expiry date. In some implementations the expiry date is updated at the time of the hand-held medical-data capture-device  104  recalibration check. 
     Error/Issue monitoring: The bridge  102  is required to track the issues/errors reported by a given device and present that information to the operator in terms of a system report. Reporting of device level errors dynamically for a given device is diagnostics tool for system management. Providing the issue/error history for a given device will provide core system diagnostic information for the hand-held medical-data capture-device  104 . 
     Battery Life monitoring: The bridge  102  is required to track the battery level of a given device and report the that information to the operator. EMR capture system  100  is to highlight to the operator that a given device has an expired or nearly expired or failed internal battery based on the information exposed by the hand-held medical-data capture-device  104 . It is the hand-held medical-data capture-devices  104  responsibility to determine its own internal power source charge level or battery condition. The bridge  102  can provide a mechanism to report the known battery condition for all devices, e.g. say all devices that have 10% battery level remaining. 
     Lost/Stolen/Failed monitoring: The bridge  102  is required to determine for a given hand-held medical-data capture-device  104  if it has been lost/stolen/or failed and disable the hand-held medical-data capture-device  104  for system operation. Being able to determine if a system has not connected to the bridge  102  for a period of time is a feature for failed, lost or stolen reporting to the operator. If a hand-held medical-data capture-device  104  has not connected to EMR capture system  100  for a period of time, EMR capture system  100  determines that the hand-held medical-data capture-device  104  has been stolen or lost, in this event the operator is informed in terms of a system report and the hand-held medical-data capture-device  104  removed from the supported devices list. If and when the hand-held medical-data capture-device  104  reconnects to EMR capture system  100  the hand-held medical-data capture-device  104  is to be lighted as “detected” and forced to be rechecked and re-commissioned again for use on the network. 
     Device Keep Alive: The bridge  102  provides a mechanism to inform a target hand-held medical-data capture-device  104  upon connection to the bridge  102  to stay connected to the bridge  102  until released by the bridge  102 . A hand-held medical-data capture-device  104  keep alive method in some implementations is provided so that the bridge  102  when a hand-held medical-data capture-device  104  connects can inform the hand-held medical-data capture-device  104  to stay powered and connected to the bridge  102  for the purposes of reconfiguration, status monitoring or diagnostics. 
     Reset device to network default: A method to reset a target device or group of selected devices to factory settings for all network parameters in some implementations be supported. 
     Reset device to factory default: A method to reset a target device or group of selected devices to their factory default settings in some implementations is supported. 
     Dynamic Device Parameter Configuration: The bridge  102  provides a mechanism to provide configuration information to a hand-held medical-data capture-device  104  when requested by the hand-held medical-data capture-device  104  on connection to the bridge  102  or via the keep device alive mechanism. Upon connecting to a bridge  102  a hand-held medical-data capture-device  104  as part of the communications protocol will determine if its current configuration is out of date, if any aspect of the hand-held medical-data capture-devices  104  configuration is out of date and is required to be updated then the bridge  102  provides the current configuration information for the hand-held medical-data capture-device  104  model and revision. This is intended to be as simple as the hand-held medical-data capture-device  104  getting the configuration setting for each of its supported parameters. the bridge  102  is responsible to ensure that the supplied information is correct for the hand-held medical-data capture-device  104  model and revision level. 
     Device Configuration Grouping: Single device: The bridge  102  provides a mechanism to configure a single device, based on unique device id, to known configuration parameters. The bridge  102  in some implementations allows a single hand-held medical-data capture-device  104  to be updated when it connects to the bridge  102  either via the heart beat method or via operator use. This effectively means that the bridge  102  provides a method to manage and maintain individual device configuration settings and have those settings available dynamically for when the hand-held medical-data capture-device  104  connects. Further the bridge  102  is required to support per device configurations for different revisions of device firmware, for example rev 1 of device A has configure parameters x,y and z, but revision 2 of the hand-held medical-data capture-device  104  has configuration parameters has x,y,z and k and the valid allowed range for the y parameter has been reduced. 
     Device Configuration Grouping—Hand-held medical-data capture-device  104  model group: The bridge  102  provides a mechanism to configure all devices within a model range to known configuration parameters. The facility to reconfigure a selected sub-group of devices that are model x and at revision level all with the same configuration information. 
     Device Configuration Grouping—selected group within model range: The bridge  102  provides a mechanism to configure a selected number of devices within the same model range to known configuration parameters. The facility to reconfigure a selected sub-group of devices that are model x and at revision level y Device Configuration Grouping—defined sub group: The bridge  102  provides a mechanism to configure a selected number of devices with the same model based on device characteristics e.g. revision level, operational location etc. The facility to reconfigure all devices that are model x and at revision level y, OR all model x devices that are in operation in Ward 6 is a feature. 
     Device Configuration files: The bridge  102  provides a method to save, load, update and edit a configuration file for a hand-held medical-data capture-device  104  model number and/or group settings. The ability to save and load configuration files and change the configuration content in the file is a required feature for EMR capture system  100 . A file management mechanism in some implementations is also provided for the saved configuration files. 
     Dynamic configuration content: The bridge  102  in some implementations dynamically per hand-held medical-data capture-device  104  connection determine upon request by the hand-held medical-data capture-device  104  the new configuration settings for that device, Given that the medial devices will connect in a random manner to the bridge  102 , the bridge  102  is required for the connected device, model, revision, unique ID etc. to maintain the configuration settings for that device. 
     Association of hand-held medical-data capture-device  104  to target EMR capture system(s)  100 : The bridge  102  provides a mechanism to control the patient record received from a hand-held medical-data capture-device  104  to transfer the record to one of more of the supported EMR capture system(s)  100 . Where more than one EMR capture system  100  is maintained by a single organization, e.g. one for ER, cardiology use and possibility one for outpatients etc. EMR capture system  100  in some implementations manage either by specific device configuration or bridge  102  configuration which EMR the patient record is to be transmitted to by the bridge  102 . 
     Device Configuration and Status Display: In some implementations, when a hand-held medical-data capture-device  104  connects to the bridge  102  that the hand-held medical-data capture-device  104  will query its current configuration settings against the bridge  102  settings for that specific device type and device as outlined below: 1. A given device based on a unique id for that device. Note each device is required to be uniquely identified in EMR capture system  100 . 2. A group of devices allocated to a physical location in the hospital, ie. Based on a ward number of other unique location reference. Accordingly, in some implementations a group of devices in a given location in some implementations is updated separately from other devices of the same type located in a different location in the same hospital environment, i.e. recovery ward 1 as opposed to ER 1. 3. A group of devices based on product type, i.e. all MD3 hand-held medical-data capture-device(s)  104 , updated with the same settings. Bridge  102  device configuration options adjusted based on hand-held medical-data capture-device  104 . The bridge  102  in some implementations adjusts the configuration options presented to the operator based on the capabilities of the hand-held medical-data capture-device  104  being configured. Where multiple different hand-held medical-data capture-devices  104  are supported by the EMR capture system  100  it cannot be assumed that each device from a different manufacture or from the same manufacture but a different model will have the same device level configuration parameters. Therefore the bridge  102  in some implementations determine the configuration capabilities for the hand-held medical-data capture-device  104  to be configured and present only valid configuration options for that device with valid para ranges for these options. 
     Device parameter Validation: The bridge  102  provides a mechanism for a given model of hand-held medical-data capture-device  104  to validate that a given configuration parameter is set within valid parameter ranges for that device model and revision. The bridge  102  is required, based on the hand-held medical-data capture-device  104  model and revision level, to present valid parameter ranges for the operator to configure a hand-held medical-data capture-device  104  level parameter. Device patient record acceptance check response source. The bridge  102  provides a mechanism to configure the hand-held medical-data capture-device  104  to require either: 1. A confirmation from the bridge  102  device only that a patient record has been received for processing 2. A confirmation from the bridge  102  device that the EMR capture system  100  has received and saved the patient information. In some implementations of the configuration of the hand-held medical-data capture-device  104  the hand-held medical-data capture-device  104  reports to the operator a status indicator. 
     Device Hospital/Clinic Reference: A device setting to allow an organization identifier to be configured on the hand-held medical-data capture-device  104 . The hand-held medical-data capture-device  104  can be configured with an alphanumeric identification string, max 30 characters that allows the organization to indicate to the Hospital/clinic that the hand-held medical-data capture-device  104  is in use with, e.g. “Boston General”. 
     Device Ward Location reference: A device setting to allow an operational location identifier to be configured on the hand-held medical-data capture-device  104 . The hand-held medical-data capture-device  104  is to be configured with an alphanumeric identification string, max 30 characters that allows the organization to indicate an operational area within the organization, e.g. “General Ward #5”. 
     Device Asset Number: A device setting to allow an organization asset number to be configured on the hand-held medical-data capture-device  104  The hand-held medical-data capture-device  104  is to be configured with an alphanumeric identification string, max 30 characters to allow the organization to provide an asset tag for the hand-held medical-data capture-device  104 . 
     Display device Manufacture Name, Device Model and Serial Number: A method to display the manufacture name, device model number and device serial number for the unit is provided. EMR capture system  100  can provide a method to determine the manufacture name, model number and device level serial number for the hand-held medical-data capture-device  104  for display purposes only, alphanumeric identification string, max 60 characters in length for each of the three parameters. 
     Display hand-held medical-data capture-device  104  unique ID reference tag: A method to display the device level unique identifier for the unit. For regulatory traceability reasons, each device is to support a unique identification number this number in some implementations be displayed by the EMR capture system  100 . In some implementations, an alphanumeric identification string is a maximum of 120 characters. This parameter is not to be updateable by the EMR capture system  100 . 
     Device last Check/Calibration Date: A method to display and set the date of the last check or re-calibration action for the hand-held medical-data capture-device  104 . This will allow the bridge  102  to determine which devices are required to be re-checked and present that information to the operator of EMR capture system  100 . All hand-held medical-data capture-devices  104  with a measurement function are required to be checked for accuracy on a regular basis. EMR capture system  100  provides a mechanism to update the hand-held medical-data capture-device  104  date of last check/calibration when a device level check has been carried out. 
     Device Temperature Display units: Configuration option for the displayed temperature units for the hand-held medical-data capture-device  104 , Centigrade or Fahrenheit. For patient temperature result the unit in some implementations be configured for reporting temperatures in degrees centigrade or Fahrenheit. Default is: Fahrenheit. Note this is for device level reporting to the operator for the hand-held medical-data capture-device  104 , the hand-held medical-data capture-device  104  will report all temperatures in Kelvin. The bridge  102  will also require a configuration parameter for the display of any temperature results. 
     Operator scan enable/disable: The bridge  102  can provide a mechanism to enable or disable the hand-held medical-data capture-device  104  level operator ID scan action. The operator ID scan capability is to be configurable on a per device basis so that it can be enabled or disabled. Allow Operator Scan Repeat for more than one patient scan: The bridge  102  can provide a mechanism to enable/disable the hand-held medical-data capture-device  104  to take a single operator ID scan and associate that id with multiple patient measurements. Where the clinical work flow allows for a known number of patient scans, or predetermined time frame, to be taken by a single operator an enable/disable feature for the hand-held medical-data capture-device  104  is to be provided. Default is: disabled Max number of patient scans per operator scan: The bridge  102  can provide a configuration parameter for controlling the number of patient ID scans after an operator ID scan before the operator ID scan has to be taken again by the hand-held medical-data capture-device  104 . A number 1 to 12 The number of patient scans that are allowed to be taken by the hand-held medical-data capture-device  104  and assigned the same operator ID Default: 1 Max time for multiple patient scans to one operator scan: The bridge  102  can provide a configuration parameter for controlling the time frame in seconds that a single operator ID scan can be used for multiple patient ID scans. A time limit in seconds 0 to (30*60) seconds, to allow a hand-held medical-data capture-device  104  to associate a single operator ID with multiple patient records in this time. In some implementations, a parameter of 0 disables the time limit range checking. The default is 0. 
     Digital Non-Touch Thermometers and Vital Sign Motion Amplification Detectors Apparatus Implementations 
       FIG. 3  is a block diagram of a non-touch biologic detector  300  that includes a digital infrared sensor, according to an implementation. Non-touch biologic detector  300  is an apparatus to measure temperature and other vital signs. 
     The non-touch biologic detector  300  includes a microprocessor  302 . The non-touch biologic detector  300  includes a battery  304 , a single button  306  and a digital infrared sensor  308  that is operably coupled to the microprocessor  302 . The digital infrared sensor  308  includes digital ports  310  that provide only digital readout signal  312 . The non-touch biologic detector  300  includes a display device  314  that is operably coupled to the microprocessor  302 . The microprocessor  302  is operable to receive from the digital ports  310  that provide only digital readout signal  312 . The digital readout signal  312  is representative of an infrared signal  316  detected by the digital infrared sensor  308 . A temperature estimator  318  in the microprocessor  302  is operable to estimate the temperature  320  from the digital readout signal  312  that is representative of the infrared signal  316 , a representation of an ambient air temperature reading from an ambient air sensor  322 , a representation of a calibration difference from a memory location that stores a calibration difference  324  and a memory location that stores a representation of a bias  326  in consideration of a temperature sensing mode. 
     Some implementations of the non-touch biologic detector  300  include a solid-state image transducer  328  that is operably coupled to the microprocessor  302  and is operable to provide two or more images  330  to a temporal-variation-amplifier  332  and a vital sign generator  334  in the microprocessor  302  to estimate one or more vital signs  336  that are displayed on the display device  314 . 
     The non-touch biologic detector  300  also includes a wireless communication subsystem  338  or other external communication subsystem such as an Ethernet port, that provides communication to the EMR capture system  100 . In some implementations, the wireless communication subsystem  338  is communication subsystem  3204  in  FIG. 41 . 
     In some implementations, the digital IR sensor  308  is a low noise amplifier, 17-bit ADC and powerful DSP unit through which high accuracy and resolution of the estimated body temperature  320  by the apparatus in  FIGS. 3-5, 8, 11-12 and 35-39  is achieved. 
     In some implementations, the digital IR sensor  308 , 10-bit pulse width modulation (PWM) is configured to continuously transmit the measured temperature in range of −20 . . . 120° C., with an output resolution of 0.14° C. The factory default power on reset (POR) setting is SMBus. 
     In some implementations, the digital IR sensor  308  is packaged in an industry standard TO-39 package. 
     In some implementations, the generated object and ambient temperatures are available in RAM of the digital IR sensor  308  with resolution of 0.01° C. The temperatures are accessible by 2 wire serial SMBus compatible protocol (0.02° C. resolution) or via 10-bit PWM (Pulse Width Modulated) output of the digital IR sensor  308 . 
     In some implementations, the digital IR sensor  308  is factory calibrated in wide temperature ranges: −40 . . . 85° C. for the ambient temperature and −70 . . . 380° C. for the object temperature. 
     In some implementations of the digital IR sensor  308 , the measured value is the average temperature of all objects in the Field Of View (FOV) of the sensor. In some implementations, the digital IR sensor  308  has a standard accuracy of ±0.5° C. around room temperatures, and in some implementations, the digital IR sensor  308  has an accuracy of ±0.2° C. in a limited temperature range around the human body temperature. 
     These accuracies are only guaranteed and achievable when the sensor is in thermal equilibrium and under isothermal conditions (there are no temperature differences across the sensor package). The accuracy of the detector can be influenced by temperature differences in the package induced by causes like (among others): Hot electronics behind the sensor, heaters/coolers behind or beside the sensor or by a hot/cold object very close to the sensor that not only heats the sensing element in the detector but also the detector package. In some implementations of the digital IR sensor  308 , the thermal gradients are measured internally and the measured temperature is compensated in consideration of the thermal gradients, but the effect is not totally eliminated. It is therefore important to avoid the causes of thermal gradients as much as possible or to shield the sensor from the thermal gradients. 
     In some implementations, the digital IR sensor  308  is calibrated for an object emissivity of 1, but in some implementations, the digital IR sensor  308  is calibrated for any emissivity in the range 0.1 . . . 1.0 without the need of recalibration with a black body. 
     In some implementations of the digital IR sensor  308 , the PWM can be easily customized for virtually any range desired by the customer by changing the content of 2 EEPROM cells. Changing the content of 2 EEPROM cells has no effect on the factory calibration of the device. The PWM pin can also be configured to act as a thermal relay (input is To), thus allowing for an easy and cost effective implementation in thermostats or temperature (freezing/boiling) alert applications. The temperature threshold is programmable by the microprocessor  302  of the non-touch biologic detector. In a non-touch biologic detector having a SMBus system the programming can act as a processor interrupt that can trigger reading all slaves on the bus and to determine the precise condition. 
     In some implementations, the digital IR sensor  308  has an optical filter (long-wave pass) that cuts off the visible and near infra-red radiant flux is integrated in the package to provide ambient and sunlight immunity. The wavelength pass band of the optical filter is from 5.5 till 14 μm. 
     In some implementations, the digital IR sensor  308  is controlled by an internal state machine, which controls the measurements and generations of the object and ambient temperatures and does the post-processing of the temperatures to output the temperatures through the PWM output or the SMBus compatible interface. 
     Some implementations of the non-touch biologic detector includes 2 IR sensors, the output of the IR sensors being amplified by a low noise low offset chopper amplifier with programmable gain, converted by a Sigma Delta modulator to a single bit stream and fed to a DSP for further processing. The signal is treated by programmable (by means of EEPROM contend) FIR and IIR low pass filters for further reduction of the bandwidth of the input signal to achieve the desired noise performance and refresh rate. The output of the IIR filter is the measurement result and is available in the internal RAM. 3 different cells are available: One for the on-board temperature sensor and 2 for the IR sensors. Based on results of the above measurements, the corresponding ambient temperature Ta and object temperatures To are generated. Both generated temperatures have a resolution of 0.01° C. The data for Ta and To is read in two ways: Reading RAM cells dedicated for this purpose via the 2-wire interface (0.02° C. resolution, fixed ranges), or through the PWM digital output (10 bit resolution, configurable range). In the last step of the measurement cycle, the measured Ta and To are rescaled to the desired output resolution of the PWM) and the regenerated data is loaded in the registers of the PWM state machine, which creates a constant frequency with a duty cycle representing the measured data. 
     In some implementations, the digital IR sensor  308  includes a SCL pin for Serial clock input for 2 wire communications protocol, which supports digital input only, used as the clock for SMBus compatible communication. The SCL pin has the auxiliary function for building an external voltage regulator. When the external voltage regulator is used, the 2-wire protocol for a power supply regulator is overdriven. 
     In some implementations, the digital IR sensor  308  includes a slave deviceA/PWM pin for Digital input/output. In normal mode the measured object temperature is accessed at this pin Pulse Width Modulated. In SMBus compatible mode the pin is automatically configured as open drain NMOS. Digital input/output, used for both the PWM output of the measured object temperature(s) or the digital input/output for the SMBus. In PWM mode the pin can be programmed in EEPROM to operate as Push/Pull or open drain NMOS (open drain NMOS is factory default). In SMBus mode slave deviceA is forced to open drain NMOS I/O, push-pull selection bit defines PWM/Thermal relay operation. The PWM/slave deviceA pin the digital IR sensor  308  operates as PWM output, depending on the EEPROM settings. When WPWM is enabled, after POR the PWM/slave deviceA pin is directly configured as PWM output. When the digital IR sensor  308  is in PWM mode, SMBus communication is restored by a special command. In some implementations, the digital IR sensor  308  is read via PWM or SMBus compatible interface. Selection of PWM output is done in EEPROM configuration (factory default is SMBus). PWM output has two programmable formats, single and dual data transmission, providing single wire reading of two temperatures (dual zone object or object and ambient). The PWM period is derived from the on-chip oscillator and is programmable. 
     In some implementations, the digital IR sensor  308  includes a VDD pin for External supply voltage and a VSS pin for ground. 
     The microprocessor  302  has read access to the RAM and EEPROM and write access to 9 EEPROM cells (at addresses 0x00, 0x01, 0x02, 0x03, 0x04, 0x05*, 0x0E, 0x0F, 0x09). When the access to the digital IR sensor  308  is a read operation, the digital IR sensor  308  responds with 16 data bits and 8 bit PEC only if its own slave address, programmed in internal EEPROM, is equal to the SA, sent by the master. A slave feature allows connecting up to 127 devices (SA=0x00 . . . 0x07F) with only 2 wires. In order to provide access to any device or to assign an address to a slave device before slave device is connected to the bus system, the communication starts with zero slave address followed by low R/W bit. When the zero slave address followed by low R/W bit sent from the microprocessor  302 , the digital IR sensor  308  responds and ignores the internal chip code information. 
     In some implementations, two digital IR sensors  308  are not configured with the same slave address on the same bus. 
     In regards to bus protocol, after every received 8 bits the slave device should issue ACK or NACK. When a microprocessor  302  initiates communication, the microprocessor  302  first sends the address of the slave and only the slave device which recognizes the address will ACK, the rest will remain silent. In case the slave device NACKs one of the bytes, the microprocessor  302  stops the communication and repeat the message. A NACK could be received after the packet error code (PEC). A NACK after the PEC means that there is an error in the received message and the microprocessor  302  will try resending the message. PEC generation includes all bits except the START, REPEATED START, STOP, ACK, and NACK bits. The PEC is a CRC-8 with polynomial X8+X2+X1+1. The Most Significant Bit of every byte is transferred first. 
     In single PWM output mode the settings for PWM1 data only are used. The temperature reading can be generated from the signal timing as: 
     
       
         
           
             
               T 
               OUT 
             
             = 
             
               
                 ( 
                 
                   
                     
                       2 
                       ⁢ 
                       
                         t 
                         2 
                       
                     
                     T 
                   
                   × 
                   
                     ( 
                     
                       
                         T 
                         O_MAX 
                       
                       - 
                       
                         T 
                         O_MIN 
                       
                     
                     ) 
                   
                 
                 ) 
               
               + 
               
                 T 
                 O_MIN 
               
             
           
         
       
     
     where Tmin and Tmax are the corresponding rescale coefficients in EEPROM for the selected temperature output (Ta, object temperature range is valid for both Tobj1 and Tobj2 as specified in the previous table) and T is the PWM period. Tout is TO1, TO2 or Ta according to Config Register [5:4] settings. 
     The different time intervals t1 . . . t4 have following meaning: 
     t1: Start buffer. During t1 the signal is always high. t1=0.125 s×T (where T is the PWM period) 
     t2: Valid Data Output Band, 0 . . . ½T. PWM output data resolution is 10 bit. 
     t3: Error band—information for fatal error in EEPROM (double error detected, not correctable). 
     t3=0.25 s×T. Therefore a PWM pulse train with a duty cycle of 0.875 will indicate a fatal error in EEPROM (for single PWM format). FE means Fatal Error. 
     In regards to a format for extended PWM, the temperature transmitted in Data 1 field can be generated using the following equation: 
               T     OUT   ⁢           ⁢   1       =       (         4   ⁢     t   2       T     ×     (       T     MAX   ⁢           ⁢   1       -     T     MIN   ⁢           ⁢   1         )       )     +     T     MIN   ⁢           ⁢   1               For Data 2 field the equation is: 
     
       
         
           
             
               T 
               
                 OUT 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 2 
               
             
             = 
             
               
                 ( 
                 
                   
                     
                       4 
                       ⁢ 
                       
                         t 
                         5 
                       
                     
                     T 
                   
                   × 
                   
                     ( 
                     
                       
                         T 
                         
                           MAX 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           2 
                         
                       
                       - 
                       
                         T 
                         
                           MIN 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           2 
                         
                       
                     
                     ) 
                   
                 
                 ) 
               
               + 
               
                 T 
                 
                   MIN 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   2 
                 
               
             
           
         
       
     
       FIG. 4  is a block diagram of a non-touch biologic detector that includes a digital infrared sensor and that does not include an analog-to-digital converter, according to an implementation. The non-touch biologic detector  400  does not include an analog-to-digital (A/D) converter  402  operably coupled between the digital infrared sensor  308  and the microprocessor  302 . The digital infrared sensor  308  also does not include analog readout ports  404 . The dashed lines of the A/D converter  402  and the analog readout ports  404  indicates absence of the A/D converter  402  and the analog readout ports  404  in the non-touch biologic detector  400 . The non-touch biologic detector  400  includes a microprocessor  302 . The non-touch biologic detector  400  includes a battery  304 , a single button  306 , a display device  314  and a digital infrared sensor  308  that is operably coupled to the microprocessor  302 . No analog-to-digital converter is operably coupled between the digital infrared sensor  308  and the microprocessor  302 . The digital infrared sensor  308  has only digital ports  310  and the digital infrared sensor  308  has no analog sensor readout ports. The microprocessor  302  is operable to receive from the digital ports  310  a digital readout signal  312  that is representative of an infrared signal  316  detected by the digital infrared sensor  308  and to determine the temperature  320  from the digital readout signal  312  that is representative of the infrared signal  316 . 
     Some implementations of the non-touch biologic detector  400  include a solid-state image transducer  328  that is operably coupled to the microprocessor  302  and is operable to provide two or more images  330  to a temporal-variation-amplifier  332  and a vital sign generator  334  in the microprocessor  302  to estimate one or more vital signs  336  that are displayed on the display device  314 . 
     The non-touch biologic detector  400  also includes a wireless communication subsystem  338  or other external communication subsystem such as an Ethernet port, that provides communication to the EMR capture system  100 . In some implementations, the wireless communication subsystem  338  is communication subsystem  3204  in  FIG. 41 . 
       FIG. 5  is a block diagram of a non-touch biologic detector  500  that includes a digital infrared sensor and a color display device, according to an implementation. In  FIG. 5 , the display device  314  of  FIG. 3  is a LED color display device  502 . 
     The non-touch biologic detector  500  also includes a wireless communication subsystem  338  or other external communication subsystem such as an Ethernet port, that provides communication to the EMR capture system  100 . In some implementations, the wireless communication subsystem  338  is communication subsystem  3204  in  FIG. 41 . 
     Non-Touch Cubic-Estimation Thermometers Apparatus Implementations 
       FIG. 6  is a block diagram of apparatus  600  that estimates a body core temperature of an external source point from a non-touch electromagnetic sensor, according to an implementation. The apparatus  600  includes a battery  304 , a single button  306 , a display device  314 , a non-touch electromagnetic sensor  602  and an ambient air sensor  322  that are operably coupled to the microprocessor  604 . The microprocessor  604  is operable to receive a representation of an infrared signal  316  of the external source point from the non-touch electromagnetic sensor  602 . The microprocessor  604  includes a cubic temperature estimator  606  that is operable to estimate the body core temperature  612  of the subject from the representation of the electromagnetic energy of the external source point. 
     The cubic temperature estimator  606  that estimates body temperature in reference to a cubic relationship that represents three thermal ranges between the body core temperature and the numerical representation of the electromagnetic energy of the external source point. The cubic relationship includes a coefficient representative of different relationships between the external source point and the body core temperature in the three thermal ranges in reference to the numerical representation of the electromagnetic energy of the external source point, numerical constants for each cubic factor, ambient air temperature and the three thermal ranges. The cubic relationship for all ranges of ambient temperatures provides best results because a linear or a quadratic relationship provide inaccurate estimates of body temperature, yet a quartic relationship, a quintic relationship, sextic relationship, a septic relationship or an octic relationship provide estimates along a highly irregular curve that is far too wavy or twisting with relatively sharp deviations from one ambient temperature to another ambient temperature. 
     The non-touch electromagnetic sensor  602  detects temperature in response to remote sensing of a surface a human or animal. In some implementations, the non-touch thermometer is an infrared temperature sensor. All humans or animals radiate infrared energy. The intensity of this infrared energy depends on the temperature of the human or animal, thus the amount of infrared energy emitted by a human or animal can be interpreted as a proxy or indication of the temperature of the human or animal. The non-touch electromagnetic sensor  602  measures the temperature of a human or animal based on the electromagnetic energy radiated by the human or animal. The measurement of electromagnetic energy is taken by the non-touch electromagnetic sensor  602  which constantly analyzes and registers the ambient temperature. When the operator of apparatus in  FIG. 3  holds the non-touch electromagnetic sensor  602  about 5-8 cm (2-3 inches) from the forehead and activates the radiation sensor, the measurement is instantaneously measured. To measure a temperature using the non-touch electromagnetic sensor  602 , pushing the button  306  causes a reading of temperature measurement from the non-touch electromagnetic sensor  602  and the measured temperature is thereafter displayed on the display device  314 . 
     Body temperature of a human or animal can be measured in many surface locations of the body. Most commonly, temperature measurements are taken of the forehead, mouth (oral), inner ear (tympanic), armpit (axillary) or rectum. In addition, temperature measurements are taken of a carotid artery (the external carotid artery on the right side of a human neck). An ideal place to measure temperature is the forehead in addition to the carotid artery. When electromagnetic energy is sensed from two or more source points, for example, the forehead and the external carotid artery on the right side of a human neck, a cubic temperature estimator  606  performs one or more of the actions in the methods that are described in  FIG. 27-31 . The cubic temperature estimator  606  correlates the temperatures sensed by the non-touch electromagnetic sensor  602  from the multiple source points (e.g. the forehead and the carotid artery) to another temperature, such as a core temperature of the subject, an axillary temperature of the subject, a rectal temperature of the subject and/or an oral temperature of the subject. The cubic temperature estimator  606  can be implemented as a component on a microprocessor, such as main processor  3202  in  FIG. 32 , processing unit  3304  in  FIG. 33  or microprocessor  3504  in  FIG. 35  or on a memory such as flash memory  3208  in  FIG. 32  or system memory  3306 . 
     The apparatus  600  also detects the body temperature of a human or animal regardless of the room temperature because the measured temperature of the non-touch electromagnetic sensor  602  is adjusted in reference to the ambient temperature in the air in the vicinity of the apparatus. The human or animal must not have undertaken vigorous physical activity prior to temperature measurement in order to avoid a misleading high temperature. Also, the room temperature should be moderate, 50° F. to 120° F. 
     The apparatus  600  provides a non-invasive and non-irritating means of measuring human or animal temperature to help ensure good health. 
     When evaluating results, the potential for daily variations in temperature can be considered. In children less than 6 months of age daily variation is small. In children 6 months to 4 years old the variation is about 1 degree. By age 6 variations gradually increase to 4 degrees per day. In adults there is less body temperature variation. 
     The apparatus  600  also includes a wireless communication subsystem  338  or other external communication subsystem such as an Ethernet port, that provides communication to the EMR capture system  100 . In some implementations, the wireless communication subsystem  338  is communication subsystem  3204  in  FIG. 41 . 
       FIG. 7  is a block diagram of apparatus  700  to estimate a body core temperature from an external source point from an analog infrared sensor, according to an implementation. The apparatus  700  includes a battery  304 , a single button  306 , a display device  314 , an analog infrared sensor  702  and an ambient air sensor  322  that are operably coupled to the microprocessor  604 . The microprocessor  604  is operable to receive a representation of an infrared signal  316  of the external source point from the analog infrared sensor  702 . The microprocessor  604  includes a cubic temperature estimator  606  that is operable to estimate the body core temperature  612  of the subject from the representation of the electromagnetic energy of the external source point. 
     The apparatus  700  also includes a wireless communication subsystem  338  or other external communication subsystem such as an Ethernet port, that provides communication to the EMR capture system  100 . In some implementations, the wireless communication subsystem  338  is communication subsystem  3204  in  FIG. 41 . 
       FIG. 8  is a block diagram of apparatus  800  to estimate a body core temperature from an external source point from a digital infrared sensor, according to an implementation. The apparatus  800  includes a battery  304 , a single button  306 , a display device  314 , a digital infrared sensor  308  and an ambient air sensor  322  that are operably coupled to the microprocessor  604 . The microprocessor  604  is operable to receive a representation of an infrared signal  316  of the external source point from the digital infrared sensor  308 . The microprocessor  604  includes a cubic temperature estimator  606  that is operable to estimate the body core temperature  612  of the subject from the representation of the electromagnetic energy of the external source point. 
     The apparatus  800  also includes a wireless communication subsystem  338  or other external communication subsystem such as an Ethernet port, that provides communication to the EMR capture system  100 . In some implementations, the wireless communication subsystem  338  is communication subsystem  3204  in  FIG. 41 . 
     Non-Touch Cubic-Estimation Thermometer and Vital Sign Detection Apparatus Implementations 
       FIG. 9  is a block diagram of apparatus  900  that estimates a body core temperature of an external source point from a non-touch electromagnetic sensor and that detects vital-signs from images captured by a solid-state image transducer, according to an implementation. The apparatus  900  includes a battery  304 , a single button  306 , a display device  314 , a non-touch electromagnetic sensor  902  and an ambient air sensor  322  that are operably coupled to the microprocessor  902 . The microprocessor  902  is operable to receive a representation of an infrared signal  316  of the external source point from the non-touch electromagnetic sensor  902 . The microprocessor  902  includes a cubic temperature estimator  606  that is operable to estimate the body core temperature  612  of the subject from the representation of the electromagnetic energy of the external source point. The apparatus  900  includes a solid-state image transducer  328  that is operably coupled to the microprocessor  902  and is operable to provide two or more images  330  to the microprocessor  902 . 
     The apparatus  900  also includes a wireless communication subsystem  338  or other external communication subsystem such as an Ethernet port, that provides communication to the EMR capture system  100 . In some implementations, the wireless communication subsystem  338  is communication subsystem  3204  in  FIG. 41 . 
       FIG. 10  is a block diagram of apparatus  1000  that estimates a body core temperature of an external source point from an analog infrared sensor and that detects vital-signs from images captured by a solid-state image transducer, according to an implementation. The apparatus  1000  includes a battery  304 , a single button  306 , a display device  314 , an analog infrared sensor  702  and an ambient air sensor  322  that are operably coupled to the microprocessor  902 . The microprocessor  902  is operable to receive a representation of an infrared signal  316  of the external source point from the analog infrared sensor  702 . The microprocessor  902  includes a cubic temperature estimator  606  that is operable to estimate the body core temperature  612  of the subject from the representation of the electromagnetic energy of the external source point. The apparatus  1000  includes a solid-state image transducer  328  that is operably coupled to the microprocessor  902  and is operable to provide two or more images  330  to the microprocessor  902 . 
     The apparatus  1000  also includes a wireless communication subsystem  338  or other external communication subsystem such as an Ethernet port, that provides communication to the EMR capture system  100 . In some implementations, the wireless communication subsystem  338  is communication subsystem  3204  in  FIG. 41 . 
       FIG. 11  is a block diagram of apparatus  1100  that estimates a body core temperature of an external source point from a digital infrared sensor and that detects vital-signs from images captured by a solid-state image transducer, according to an implementation. The apparatus  1100  includes a battery  304 , a single button  306 , a display device  314 , a digital infrared sensor  308  and an ambient air sensor  322  that are operably coupled to the microprocessor  902 . The microprocessor  902  is operable to receive a representation of an infrared signal  316  of the external source point from the digital infrared sensor  308 . The microprocessor  902  includes a cubic temperature estimator  606  that is operable to estimate the body core temperature  612  of the subject from the representation of the electromagnetic energy of the external source point. The apparatus  1100  includes a solid-state image transducer  328  that is operably coupled to the microprocessor  902  and is operable to provide two or more images  330  to the microprocessor  902 . 
     The apparatus  1100  also includes a wireless communication subsystem  338  or other external communication subsystem such as an Ethernet port, that provides communication to the EMR capture system  100 . In some implementations, the wireless communication subsystem  338  is communication subsystem  3204  in  FIG. 41 . 
       FIG. 12  is a block diagram of apparatus  1200  that estimates a body core temperature of an external source point from a digital infrared sensor, that does not include an analog-to-digital converter and that detects vital-signs from images captured by a solid-state image transducer, according to an implementation. The apparatus  1200  includes a battery  304 , a single button  306 , a display device  314 , a digital infrared sensor  308  and an ambient air sensor  322  that are operably coupled to the microprocessor  902 . The microprocessor  902  is operable to receive a representation of an infrared signal  316  of the external source point from the digital infrared sensor  308 . The microprocessor  902  includes a cubic temperature estimator  606  that is operable to estimate the body core temperature  612  of the subject from the representation of the electromagnetic energy of the external source point. The apparatus  1200  includes a solid-state image transducer  328  that is operably coupled to the microprocessor  902  and is operable to provide two or more images  330  to the microprocessor  902 . The apparatus  900  does not include an analog-to-digital (A/D) converter  402  operably coupled between the digital infrared sensor  308  and the microprocessor  902 . The digital infrared sensor  308  also does not include analog readout ports  404 . The dashed lines of the analog-to-digital (A/D) converter  402  and the analog readout ports  404  indicates absence of the A/D converter  402  and the analog readout ports  404  in the apparatus  900 . 
     The apparatus  1200  also includes a wireless communication subsystem  338  or other external communication subsystem such as an Ethernet port, that provides communication to the EMR capture system  100 . In some implementations, the wireless communication subsystem  338  is communication subsystem  3204  in  FIG. 41 . 
     In regards to the structural relationship of the digital infrared sensor  308  and the microprocessor  302  in  FIGS. 3-5, 8 and 11-12 , heat radiation on the digital infrared sensor  308  from any source such as the microprocessor  302  or heat sink, will distort detection of infrared energy by the digital infrared sensor  308 . In order to prevent or at least reduce heat transfer between the digital infrared sensor  308  and the microprocessor  302 , the apparatus in  FIGS. 3-5, 8 and 11-12  are low-powered devices and thus low heat-generating devices that are also powered by a battery  304 ; and that are only used for approximately a 5 second period of time for each measurement (1 second to acquire the temperature samples and generate the body core temperature result, and 4 seconds to display that result to the operator) so there is little heat generated by the apparatus in  FIGS. 3-5, 8 and 11-12  in active use. 
     The internal layout of the apparatus in  FIGS. 3-5, 8 and 11-12  minimizes as practically as possible the digital infrared sensor as far away in distance from all other components such the microprocessor ( 302 ,  604  or  902 ) within the practical limitations of the industrial design of the apparatus in  FIGS. 3-5, 8 and 11-12 . 
     More specifically, to prevent or at least reduce heat transfer between the digital infrared sensor  308  and the microprocessor ( 102 ,  604  or  902 ) in some implementations the digital infrared sensor  308  is isolated on a separate PCB from the PCB that has the microprocessor ( 102 ,  604  or  902 ), as shown in  FIG. 34 , and the two PCBs are connected by only a connector that has 4 pins. The minimal connection of the single connector having 4 pins reduces heat transfer from the microprocessor ( 102 ,  604  or  902 ) to the digital infrared sensor  308  through the electrical connector and through transfer that would occur through the PCB material if the digital infrared sensor  308  and the microprocessor  302  were mounted on the same PCB. 
     In some implementations, the apparatus in  FIG. 3-12  includes only one printed circuit board, in which case the printed circuit board includes the microprocessor  302  and the digital infrared sensor  308 , non-touch electromagnetic sensor  602  or the analog infrared sensor  702  are mounted on the singular printed circuit board. In some implementations, the apparatus in  FIG. 3-12  includes two printed circuit boards, such as a first printed circuit board and a second printed circuit board in which the microprocessor  302  is on the first printed circuit board and the digital infrared sensor  308 , non-touch electromagnetic sensor  602  or the analog infrared sensor  702  are on the second printed circuit board. In some implementations, the apparatus in  FIG. 3-12  includes only one display device  314 , in which case the display device  314  includes not more than one display device  314 . In some implementations, the display device  314  is a liquid-crystal diode (LCD) display device. In some implementations, the display device  314  is a light-emitting diode (LED) display device. In some implementations, the apparatus in  FIG. 3-12  includes only one battery  304 . 
     Digital Infrared Thermometer Method Implementations 
     In the previous section, apparatus of the operation of an implementation was described. In this section, the particular methods performed by  FIGS. 3-5, 8 and 11-12  are described by reference to a series of flowcharts. 
       FIG. 13  is a flowchart of a method  1300  to determine a temperature from a digital infrared sensor, according to an implementation. Method  1300  includes receiving from the digital readout ports of a digital infrared sensor a digital signal that is representative of an infrared signal detected by the digital infrared sensor, at block  1302 . No signal that is representative of the infrared signal is received from an analog infrared sensor. 
     Method  1300  also includes determining a temperature from the digital signal that is representative of the infrared signal, at block  1304 . 
       FIG. 14  is a flowchart of a method  1400  to display temperature color indicators, according to an implementation of three colors. Method  1400  provides color rendering in the color LED  3412  to indicate a general range of a temperature. 
     Method  1400  includes receiving a temperature (such as temperature  340  in  FIG. 3 ), at block  1401 . 
     Method  1400  also includes determining whether or not the temperature is in the range of 32.0° C. and 37.3° C., at block  1402 . If the temperature is in the range of 32.0° C. and 37.3° C., then the color is set to ‘amber’ to indicate a temperature that is low, at block  1404  and the background of the color LED  3412  is activated in accordance with the color, at block  1406 . 
     If the temperature is not the range of 32.0° C. and 37.3° C., then method  1400  also includes determining whether or not the temperature is in the range of 37.4° C. and 38.0° C., at block  1408 . If the sensed temperature is in the range of 37.4° C. and 38.0° C., then the color is set to green to indicate no medical concern, at block  1410  and the background of the color LED  3412  is activated in accordance with the color, at block  1406 . 
     If the temperature is not the range of 37.4° C. and 38.0° C., then method  1400  also includes determining whether or not the temperature is over 38.0° C., at block  1412 . If the temperature is over 38.0° C., then the color is set to ‘red’ to indicate alert, at block  1412  and the background of the color LED  3412  is activated in accordance with the color, at block  1406 . 
     Method  1400  assumes that temperature is in gradients of 10ths of a degree. Other temperature range boundaries are used in accordance with other gradients of temperature sensing. 
     In some implementations, some pixels in the color LED  3412  are activated as an amber color when the temperature is between 36.3° C. and 37.3° C. (97.3° F. to 99.1° F.), some pixels in the color LED  3412  are activated as a green when the temperature is between 37.4° C. and 37.9° C. (99.3° F. to 100.2° F.), some pixels in the color LED  3412  are activated as a red color when the temperature is greater than 38° C. (100.4° F.). In some implementations, the color LED  3412  is a backlit LCD screen  502  in  FIG. 5  (which is easy to read in a dark room) and some pixels in the color LED  3412  are activated (remain lit) for about 5 seconds after the single button  306  is released. After the color LED  3412  has shut off, another temperature reading can be taken by the apparatus. The color change of the color LED  3412  is to alert the operator of the apparatus of a potential change of body temperature of the human or animal subject. The temperature reported on the display can be used for treatment decisions. 
       FIG. 15  is a flowchart of a method  1500  to manage power in a non-touch device having a digital infrared sensor, according to an implementation. The method  1500  manages power in the device, such as non-touch biologic detectors and thermometers in  FIG. 3-12 , the non-touch thermometer  3500  in  FIG. 35 , the hand-held device  3200  in  FIG. 32  and/or the computer  3300  in  FIG. 33  in order to reduce heat pollution in the digital infrared sensor. 
     To prevent or at least reduce heat transfer between the digital infrared sensor  308  and the microprocessor  302 , microprocessor  604 , microprocessor  3504  In  FIG. 35 , main processor  3202  in  FIG. 32  or processing unit  3304  in  FIG. 33 , the components of the non-touch biologic detectors  300 ,  400  and  300  in  FIG. 3-12 , the non-touch thermometer  3500  in  FIG. 35 , the hand-held device  3200  in  FIG. 32  and/or the computer  3300  in  FIG. 33  are power controlled, i.e. the non-touch biologic detectors  300 ,  400  and  300  in  FIG. 3-12 , the non-touch thermometer  3500  in  FIG. 35 , the hand-held device  3200  in  FIG. 32  and/or the computer  3300  in  FIG. 33  turn sub-systems on and off, and the components are only activated when needed in the measurement and display process, which reduces power consumption and thus heat generation by the microprocessor  302 , microprocessor  3504  In  FIG. 35 , main processor  3202  in  FIG. 32  or processing unit  3304  in  FIG. 33 , of the non-touch biologic detectors  300 ,  400  and  300  in  FIG. 3-12 , the non-touch thermometer  3500  in  FIG. 35 , the hand-held device  3200  in  FIG. 32  and/or the computer  3300  in  FIG. 33 , respectively. When not in use, at block  1502 , the non-touch biologic detectors  300 ,  400  and  300  in  FIG. 3-12 , the non-touch thermometer  3500  in  FIG. 35 , the hand-held device  3200  in  FIG. 32  and/or the computer  3300  in  FIG. 33  are completely powered-off, at block  1504  (including the main PCB having the microprocessor  302 , microprocessor  604 , microprocessor  3504  In  FIG. 35 , main processor  3202  in  FIG. 32  or processing unit  3304  in  FIG. 33 , and the sensor PCB having the digital infrared sensor  308 ) and not drawing any power, other than a power supply, i.e. a boost regulator, which has the effect that the non-touch biologic detectors  300 ,  400  and  300  in  FIG. 3-12 , the non-touch thermometer  3500  in  FIG. 35 , the hand-held device  3200  in  FIG. 32  and/or the computer  3300  in  FIG. 33  draw only drawing micro-amps from the battery  304  while in the off state, which is required for the life time requirement of 3 years of operation, but which also means that in the non-use state there is very little powered circuitry in the non-touch biologic detectors  300 ,  400  and  300  in  FIG. 3-12 , the non-touch thermometer  3500  in  FIG. 35 , the hand-held device  3200  in  FIG. 32  and/or the computer  3300  in  FIG. 33  and therefore very little heat generated in the non-touch biologic detectors  300 ,  400  and  300  in  FIG. 3-12 , the non-touch thermometer  3500  in  FIG. 35 , the hand-held device  3200  in  FIG. 32  and/or the computer  3300  in  FIG. 33 . 
     When the non-touch biologic detectors  300 ,  400  and  300  in  FIG. 3-12 , the non-touch thermometer  3500  in  FIG. 35 , the hand-held device  3200  in  FIG. 32  and/or the computer  3300  in  FIG. 33  are started by the operator, at block  1506 , only the microprocessor  302 , microprocessor  604 , microprocessor  3504  In  FIG. 35 , main processor  3202  in  FIG. 32  or processing unit  3304  in  FIG. 33 , digital infrared sensor  308 , and low power LCD (e.g. display device  314 ) are turned on for the first 1 second, at block  1508 , to take the temperature measurement via the digital infrared sensor  308  and generate the body core temperature result via the microprocessor  302  in  FIG. 3-12 , microprocessor  3504  in  FIG. 35 , main processor  3202  in  FIG. 32  or processing unit  3304  in  FIG. 33 , at block  1510 . In this way, the main heat generating components (the LCD  314 , the main PCB having the microprocessor  302  and the sensor PCB having the digital infrared sensor  308 ), the display back-light and the temperature range indicator (i.e. the traffic light indicator  3412 ) are not on and therefore not generating heat during the critical start-up and measurement process, no more than 1 second. After the measurement process of block  1510  has been completed, the digital infrared sensor  308  is turned off, at block  1512 , to reduce current usage from the batteries and heat generation, and also the display back-light and temperature range indicators are turned on, at block  1514 . 
     The measurement result is displayed for 4 seconds, at block  1516 , and then the non-touch biologic detectors  300 ,  400  and  300  in  FIG. 3-12 , the non-touch thermometer  3500  in  FIG. 35 , the hand-held device  3200  in  FIG. 32  and/or the computer  3300  in  FIG. 33  are put in low power-off state, at block  1518 . 
     In some implementations of methods and apparatus of  FIG. 3-39  an operator can take the temperature of a subject at multiple locations on a patient and from the temperatures at multiple locations to determine the temperature at a number of other locations of the subject. The multiple source points of which the electromagnetic energy is sensed are mutually exclusive to the location of the correlated temperature. In one example, the carotid artery source point on the subject and a forehead source point are mutually exclusive to the core temperature of the subject, an axillary temperature of the subject, a rectal temperature of the subject and an oral temperature of the subject. 
     The correlation of action can include a calculation based on Formula 1:
 
 T   body   =|f   stb ( T   surface temp   +f   ntc ( T   ntc ))+ F 4 body |   Formula 1
         where T body  is the temperature of a body or subject   where f stb  is a mathematical formula of a surface of a body   where f ntc  is mathematical formula for ambient temperature reading   where T surface temp  is a surface temperature determined from the sensing.   where T ntc  is an ambient air temperature reading   where F4 body  is a calibration difference in axillary mode, which is stored or set in a memory of the apparatus either during manufacturing or in the field. The apparatus also sets, stores and retrieves F4 oral , F4 core , and F4 rectal  in the memory.   f ntc (T ntc ) is a bias in consideration of the temperature sensing mode. For example f axillary (T axillary )=0.2° C., f oral (T oral )=0.4° C., f rectal (T rectal )=0.5° C. and f core (T core )=0.3° C.       

     In some implementations of determining a correlated body temperature of carotid artery by biasing a sensed temperature of a carotid artery, the sensed temperature is biased by +0.5° C. to yield the correlated body temperature. In another example, the sensed temperature is biased by −0.5° C. to yield the correlated body temperature. An example of correlating body temperature of a carotid artery follows:
         f ntc (T ntc )=0.2° C. when T ntc =26.2° C. as retrieved from a data table for body sensing mode.   assumption: T surface temp =37.8° C.
 
 T   surface temp   +f   ntc ( T   ntc )=37.8° C.+0.2° C.=38.0° C.
 
 f   stb ( T   surface temp   +f   ntc ( T   ntc ))=38° C.+1.4° C.=39.4° C.
       

     assumption: F4 body =0.5° C.
 
 T   body   =|f   stb ( T   surface temp   +f   ntc ( T   ntc ))+ F 4 body |=|39.4° C.+0.5 C|=39.9° C.
 
     The correlated temperature for the carotid artery is 40.0° C. 
     In an example of correlating temperature of a plurality of external locations, such as a forehead and a carotid artery to an axillary temperature, first a forehead temperature is calculated using formula 1 as follows:
         f ntc (T ntc )=0.2° C. when T ntc =26.2° C. as retrieved from a data table for axillary sensing mode.   assumption: T surface temp =37.8° C.
 
 T   surface temp   +f   ntc ( T   ntc )=37.8° C.+0.2° C.=38.0° C.
 
 f   stb ( T   surface temp   +f   ntc ( T   ntc ))=38° C.+1.4° C.=39.4° C.
   assumption: F4 body =0° C.
 
 T   body   =|f   stb ( T   surface temp   +f   ntc ( T   ntc ))+ F 4 body |=|39.4° C.+0 C|=39.4° C.
       

     And second, a carotid temperature is calculated using formula 1 as follows:
         f ntc (T ntc )=0.6° C. when T ntc =26.4° C. as retrieved from a data table.   assumption: T surface temp =38.0° C.
 
 T   surface temp   +f   ntc ( T   ntc )=38.0° C.+0.6° C.=38.6° C.
 
 f   stb ( T   surface temp   +f   ntc ( T   ntc ))=38.6° C.+1.4 C=40.0° C.
   assumption: F4 body =0° C.
 
 T   body   =|f   stb ( T   surface temp   +f   ntc ( T   ntc ))+ F 4 body |=|40.0° C.+0 C|=40.0° C.
       

     Thereafter the correlated temperature for the forehead (39.4° C.) and the correlated temperature for the carotid artery (40.0° C.) are averaged, yielding the final result of the scan of the forehead and the carotid artery as 39.7° C. 
     Vital Sign Motion Amplification Apparatus Implementations 
     Apparatus in  FIG. 16-24  use spatial and temporal signal processing to generate vital signs from a series of digital images. 
       FIG. 16  is a block diagram of an apparatus  1600  of variation amplification, according to an implementation. Apparatus  1600  analyzes the temporal and spatial variations in digital images of an animal subject in order to generate and communicate biological vital signs. 
     In some implementations, apparatus  1600  includes a skin-pixel-identifier  1602  that identifies pixel values that are representative of the skin in two or more images  1604 . In some implementations the images  1604  are frames of a video. The skin-pixel-identifier  1602  performs block  2502  in  FIG. 25 . Some implementations of the skin-pixel-identifier  1602  perform an automatic seed point based clustering process on the two or more images  1604 . In some implementations, apparatus  1600  includes a frequency filter  1606  that receives the output of the skin-pixel-identifier  1602  and applies a frequency filter to the output of the skin-pixel-identifier  1602 . The frequency filter  1606  performs block  2504  in  FIG. 25  to process the images  1604  in the frequency domain. In implementations where the apparatus in  FIG. 16-24  or the methods in  FIG. 25-27  are implemented on non-touch biologic detectors and thermometers in  FIG. 3-10 , the images  1604  in  FIG. 16-24  are the images  330  in  FIG. 3-12 . In some implementations the apparatus in  FIG. 16-22  or the methods in  FIG. 25-29  are implemented on the hand-held device  3200  in  FIG. 32 . 
     In some implementations, apparatus  1600  includes a regional facial clusterial module  1608  that applies spatial clustering to the output of the frequency filter  1606 . The regional facial clusterial module  1608  performs block  2506  in  FIG. 25 . In some implementations the regional facial clusterial module  1608  includes fuzzy clustering, k-means clustering, expectation-maximization process, Ward&#39;s apparatus or seed point based clustering. 
     In some implementations, apparatus  1600  includes a frequency-filter  1610  that applies a frequency filter to the output of the regional facial clusterial module  1608 . The frequency-filter  1610  performs block  2508  in  FIG. 25 . In some implementations, the frequency-filter  1610  is a one-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. Some implementations of frequency-filter  1610  includes de-noising (e.g. smoothing of the data with a Gaussian filter). The skin-pixel-identifier  1602 , the frequency filter  1606 , the regional facial clusterial module  1608  and the frequency-filter  1610  amplify temporal variations (as a temporal-variation-amplifier) in the two or more images  1604 . 
     In some implementations, apparatus  1600  includes a temporal-variation identifier  1612  that identifies temporal variation of the output of the frequency-filter  1610 . Thus, the temporal variation represents temporal variation of the images  1604 . The temporal-variation identifier  1612  performs block  2510  in  FIG. 25 . 
     In some implementations, apparatus  1600  includes a vital-sign generator  1614  that generates one or more vital sign(s)  1616  from the temporal variation. The vital sign(s)  1616  are displayed for review by a healthcare worker or stored in a volatile or nonvolatile memory for later analysis, or transmitted to other devices for analysis. 
     Fuzzy clustering is a class of processes for cluster analysis in which the allocation of data points to clusters is not “hard” (all-or-nothing) but “fuzzy” in the same sense as fuzzy logic. Fuzzy logic being a form of many-valued logic which with reasoning that is approximate rather than fixed and exact. In fuzzy clustering, every point has a degree of belonging to clusters, as in fuzzy logic, rather than belonging completely to just one cluster. Thus, points on the edge of a cluster, may be in the cluster to a lesser degree than points in the center of cluster. An overview and comparison of different fuzzy clustering processes is available. Any point x has a set of coefficients giving the degree of being in the kth cluster w k (x). With fuzzy c-means, the centroid of a cluster is the mean of all points, weighted by a degree of belonging of each point to the cluster: 
     
       
         
           
             
               c 
               k 
             
             = 
             
               
                 
                   
                     ∑ 
                     x 
                   
                   ⁢ 
                   
                     
                       
                         
                           w 
                           k 
                         
                         ⁡ 
                         
                           ( 
                           x 
                           ) 
                         
                       
                       m 
                     
                     ⁢ 
                     x 
                   
                 
                 
                   
                     ∑ 
                     x 
                   
                   ⁢ 
                   
                     
                       
                         w 
                         k 
                       
                       ⁡ 
                       
                         ( 
                         x 
                         ) 
                       
                     
                     m 
                   
                 
               
               . 
             
           
         
       
     
     The degree of belonging, w k (x), is related inversely to the distance from x to the cluster center as calculated on the previous pass. The degree of belonging, w k (X) also depends on a parameter m that controls how much weight is given to the closest center. 
     k-means clustering is a process of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining, k-means clustering partitions n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. A Voronoi Cell being a region within a Voronoi Diagram that is a set of points which is specified beforehand. A Voronoi Diagram is a technique of dividing space into a number of regions. k-means clustering uses cluster centers to model the data and tends to find clusters of comparable spatial extent, like K-means clustering, but each data point has a fuzzy degree of belonging to each separate cluster. 
     An expectation-maximization process is an iterative process for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The expectation-maximization iteration alternates between performing an expectation step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization step, which computes parameters maximizing the expected log-likelihood found on the expectation step. These parameter-estimates are then used to determine the distribution of the latent variables in the next expectation step. 
     The expectation maximization process seeks to find the maximization likelihood expectation of the marginal likelihood by iteratively applying the following two steps: 
     1. Expectation step (E step): Calculate the expected value of the log likelihood function, with respect to the conditional distribution of Z given X under the current estimate of the parameters θ (t) :
 
 Q (θ|θ (t) )= E   Z|X,θ     (t)   [log  L (θ; X,Z )]
 
     2. Maximization step (M step): Find the parameter that maximizes this quantity: 
     
       
         
           
             
               θ 
               
                 ( 
                 
                   t 
                   + 
                   1 
                 
                 ) 
               
             
             = 
             
               
                 
                   arg 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   max 
                 
                 θ 
               
               ⁢ 
               
                   
               
               ⁢ 
               
                 Q 
                 ⁡ 
                 
                   ( 
                   
                     θ 
                     | 
                     
                       θ 
                       
                         ( 
                         t 
                         ) 
                       
                     
                   
                   ) 
                 
               
             
           
         
       
     
     Note that in typical models to which expectation maximization is applied: 
     1. The observed data points X may be discrete (taking values in a finite or countably infinite set) or continuous (taking values in an uncountably infinite set). There may in fact be a vector of observations associated with each data point. 
     2. The missing values (aka latent variables) Z are discrete, drawn from a fixed number of values, and there is one latent variable per observed data point. 
     3. The parameters are continuous, and are of two kinds: Parameters that are associated with all data points, and parameters associated with a particular value of a latent variable (i.e. associated with all data points whose corresponding latent variable has a particular value). 
     The Fourier Transform is an important image processing tool which is used to decompose an image into its sine and cosine components. The output of the transformation represents the image in the Fourier or frequency domain, while the input image is the spatial domain equivalent. In the Fourier domain image, each point represents a particular frequency contained in the spatial domain image. 
     The Discrete Fourier Transform is the sampled Fourier Transform and therefore does not contain all frequencies forming an image, but only a set of samples which is large enough to fully describe the spatial domain image. The number of frequencies corresponds to the number of pixels in the spatial domain image, i.e. the image in the spatial and Fourier domains are of the same size. 
     For a square image of size N×N, the two-dimensional DFT is given by: 
     
       
         
           
             
               F 
               ⁡ 
               
                 ( 
                 
                   k 
                   , 
                   l 
                 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   0 
                 
                 
                   N 
                   - 
                   1 
                 
               
               ⁢ 
               
                 
                   ∑ 
                   
                     j 
                     = 
                     0 
                   
                   
                     N 
                     - 
                     1 
                   
                 
                 ⁢ 
                 
                   
                     f 
                     ⁡ 
                     
                       ( 
                       
                         i 
                         , 
                         j 
                       
                       ) 
                     
                   
                   ⁢ 
                   
                     e 
                     
                       
                         - 
                         ι 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       2 
                       ⁢ 
                       
                         π 
                         ⁡ 
                         
                           ( 
                           
                             
                               ki 
                               N 
                             
                             + 
                             
                               lj 
                               N 
                             
                           
                           ) 
                         
                       
                     
                   
                 
               
             
           
         
       
     
     where f(a,b) is the image in the spatial domain and the exponential term is the basis function corresponding to each point F(k,l) in the Fourier space. The equation can be interpreted as: the value of each point F(k,l) is obtained by multiplying the spatial image with the corresponding base function and summing the result. 
     The basis functions are sine and cosine waves with increasing frequencies, i.e. F(0,0) represents the DC-component of the image which corresponds to the average brightness and F(N−1,N−1) represents the highest frequency. 
     A high-pass filter (HPF) is an electronic filter that passes high-frequency signals but attenuates (reduces the amplitude of) signals with frequencies lower than the cutoff frequency. The actual amount of attenuation for each frequency varies from filter to filter. A high-pass filter is usually modeled as a linear time-invariant system. A high-pass filter can also be used in conjunction with a low-pass filter to make a bandpass filter. The simple first-order electronic high-pass filter is implemented by placing an input voltage across the series combination of a capacitor and a resistor and using the voltage across the resistor as an output. The product of the resistance and capacitance (R×C) is the time constant (τ); the product is inversely proportional to the cutoff frequency f c , that is: 
     
       
         
           
             
               
                 f 
                 c 
               
               = 
               
                 
                   1 
                   
                     2 
                     ⁢ 
                     π 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     τ 
                   
                 
                 = 
                 
                   1 
                   
                     2 
                     ⁢ 
                     π 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     RC 
                   
                 
               
             
             , 
           
         
       
         
         
           
             where f c  is in hertz, τ is in seconds, R is in ohms, and C is in farads. 
           
         
       
    
     A low-pass filter is a filter that passes low-frequency signals and attenuates (reduces the amplitude of) signals with frequencies higher than the cutoff frequency. The actual amount of attenuation for each frequency varies depending on specific filter design. Low-pass filters are also known as high-cut filter, or treble cut filter in audio applications. A low-pass filter is the opposite of a high-pass filter. Low-pass filters provide a smoother form of a signal, removing the short-term fluctuations, and leaving the longer-term trend. One simple low-pass filter circuit consists of a resistor in series with a load, and a capacitor in parallel with the load. The capacitor exhibits reactance, and blocks low-frequency signals, forcing the low-frequency signals through the load instead. At higher frequencies the reactance drops, and the capacitor effectively functions as a short circuit. The combination of resistance and capacitance gives the time constant of the filter. The break frequency, also called the turnover frequency or cutoff frequency (in hertz), is determined by the time constant. 
     A band-pass filter is a device that passes frequencies within a certain range and attenuates frequencies outside that range. These filters can also be created by combining a low-pass filter with a high-pass filter. Bandpass is an adjective that describes a type of filter or filtering process; bandpass is distinguished from passband, which refers to the actual portion of affected spectrum. Hence, a dual bandpass filter has two passbands. A bandpass signal is a signal containing a band of frequencies not adjacent to zero frequency, such as a signal that comes out of a bandpass filter. 
       FIG. 17  is a block diagram of an apparatus  1700  of variation amplification, according to an implementation. Apparatus  1700  analyzes the temporal and spatial variations in digital images of an animal subject in order to generate and communicate biological vital signs. 
     In some implementations, apparatus  1700  includes a skin-pixel-identifier  1602  that identifies pixel values that are representative of the skin in two or more images  1604 . The skin-pixel-identifier  1602  performs block  2502  in  FIG. 25 . Some implementations of the skin-pixel-identifier  1602  performs an automatic seed point based clustering process on the least two images  1604 . 
     In some implementations, apparatus  1700  includes a frequency filter  1606  that receives the output of the skin-pixel-identifier  1602  and applies a frequency filter to the output of the skin-pixel-identifier  1602 . The frequency filter  1606  performs block  2504  in  FIG. 25  to process the images  1604  in the frequency domain. 
     In some implementations, apparatus  1700  includes a regional facial clusterial module  1608  that applies spatial clustering to the output of the frequency filter  1606 . The regional facial clusterial module  1608  performs block  2506  in  FIG. 25 . In some implementations the regional facial clusterial module  1608  includes fuzzy clustering, k-means clustering, expectation-maximization process, Ward&#39;s apparatus or seed point based clustering. 
     In some implementations, apparatus  1700  includes a frequency-filter  1610  that applies a frequency filter to the output of the regional facial clusterial module  1608 , to generate a temporal variation. The frequency-filter  1610  performs block  2508  in  FIG. 25 . In some implementations, the frequency-filter  1610  is a one-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. Some implementations of frequency-filter  1610  includes de-noising (e.g. smoothing of the data with a Gaussian filter). The skin-pixel-identifier  1602 , the frequency filter  1606 , the regional facial clusterial module  1608  and the frequency-filter  1610  amplify temporal variations in the two or more images  1604 . 
     In some implementations, apparatus  1700  includes a vital-sign generator  1614  that generates one or more vital sign(s)  1616  from the temporal variation. The vital sign(s)  1616  are displayed for review by a healthcare worker or stored in a volatile or nonvolatile memory for later analysis, or transmitted to other devices for analysis. 
       FIG. 18  is a block diagram of an apparatus  1800  of variation amplification, according to an implementation. Apparatus  1800  analyzes the temporal and spatial variations in digital images of an animal subject in order to generate and communicate biological vital signs. 
     In some implementations, apparatus  1800  includes a skin-pixel-identifier  1602  that identifies pixel values that are representative of the skin in two or more images  1604 . The skin-pixel-identifier  1602  performs block  2502  in  FIG. 25 . Some implementations of the skin-pixel-identifier  1602  performs an automatic seed point based clustering process on the least two images  1604 . 
     In some implementations, apparatus  1800  includes a spatial bandpass filter  1802  that receives the output of the skin-pixel-identifier  1602  and applies a spatial bandpass filter to the output of the skin-pixel-identifier  1602 . The spatial bandpass filter  1802  processes the images  1604  in the spatial domain. 
     In some implementations, apparatus  1800  includes a regional facial clusterial module  1608  that applies spatial clustering to the output of the frequency filter  1606 . In some implementations the regional facial clusterial module  1608  includes fuzzy clustering, k-means clustering, expectation-maximization process, Ward&#39;s apparatus or seed point based clustering. 
     In some implementations, apparatus  1800  includes a temporal bandpass filter  1804  that applies a frequency filter to the output of the regional facial clusterial module  1608 . In some implementations, the temporal bandpass filter  1804  is a one-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. Some implementations of temporal bandpass filter  1804  includes de-noising (e.g. smoothing of the data with a Gaussian filter). 
     The skin-pixel-identifier  1602 , the spatial bandpass filter  1802 , the regional facial clusterial module  1608  and the temporal bandpass filter  1804  amplify temporal variations in the two or more images  1604 . 
     In some implementations, apparatus  1800  includes a temporal-variation identifier  1612  that identifies temporal variation of the output of the frequency-filter  1610 . Thus, the temporal variation represents temporal variation of the images  1604 . 
     In some implementations, apparatus  1800  includes a vital-sign generator  1614  that generates one or more vital sign(s)  1616  from the temporal variation. The vital sign(s)  1616  are displayed for review by a healthcare worker or stored in a volatile or nonvolatile memory for later analysis, or transmitted to other devices for analysis. 
       FIG. 19  is a block diagram of an apparatus  1900  of variation amplification, according to an implementation. 
     In some implementations, apparatus  1900  includes a pixel-examiner  1902  that examines pixel values of two or more images  1604 . The pixel-examiner  1902  performs block  4802  in  FIG. 48 . 
     In some implementations, apparatus  1900  includes a temporal variation determiner  1906  that determines a temporal variation of examined pixel values. The temporal variation determiner  1906  performs block  4804  in  FIG. 48 . 
     In some implementations, apparatus  1900  includes a signal-processor  1908  that applies signal processing to the pixel value temporal variation, generating an amplified temporal variation. The signal-processor  1908  performs block  4806  in  FIG. 48 . The signal processing amplifies the temporal variation, even when the temporal variation is small. In some implementations, the signal processing performed by signal-processor  1908  is temporal bandpass filtering that analyzes frequencies over time. In some implementations, the signal processing performed by signal-processor  1908  is spatial processing that removes noise. Apparatus  1900  amplifies only small temporal variations in the signal-processing module. 
     In some implementations, apparatus  1800  includes a vital-sign generator  1614  that generates one or more vital sign(s)  1616  from the temporal variation. The vital sign(s)  1616  are displayed for review by a healthcare worker or stored in a volatile or nonvolatile memory for later analysis, or transmitted to other devices for analysis. 
     While apparatus  1900  can process large temporal variations, an advantage in apparatus  1900  is provided for small temporal variations. Therefore apparatus  1900  is most effective when the two or more images  1604  have small temporal variations between the two or more images  1604 . In some implementations, a vital sign is generated from the amplified temporal variations of the two or more images  1604  from the signal-processor  1908 . 
       FIG. 20  is a block diagram of an apparatus  2000  of variation amplification, according to an implementation. Apparatus  2000  analyzes the temporal and spatial variations in digital images of an animal subject in order to generate and communicate biological vital signs. 
     In some implementations, apparatus  2000  includes a skin-pixel-identification module  2002  that identifies pixel values  2006  that are representative of the skin in two or more images  2004 . The skin-pixel-identification module  2002  performs block  2502  in  FIG. 25 . Some implementations of the skin-pixel-identification module  2002  perform an automatic seed point based clustering process on the least two images  2004 . 
     In some implementations, apparatus  2000  includes a frequency-filter module  2008  that receives the identified pixel values  2006  that are representative of the skin and applies a frequency filter to the identified pixel values  2006 . The frequency-filter module  2008  performs block  2504  in  FIG. 25  to process the images  1604  in the frequency domain. Each of the images  1604  is Fourier transformed, multiplied with a filter function and then re-transformed into the spatial domain. Frequency filtering is based on the Fourier Transform. The operator takes an image  1604  and a filter function in the Fourier domain. The image  1604  is then multiplied with the filter function in a pixel-by-pixel fashion using the formula:
 
 G ( k,l )= F ( k,l ) H ( k,l )
 
     where F(k,l) is the input image  1604  of identified pixel values  2006  in the Fourier domain, H(k,l) the filter function and G(k,l) is the filtered image  2010 . To obtain the resulting image in the spatial domain, G(k,l) is re-transformed using the inverse Fourier Transform. In some implementations, the frequency-filter module  2008  is a two-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. 
     In some implementations, apparatus  2000  includes a spatial-cluster module  2012  that applies spatial clustering to the frequency filtered identified pixel values of skin  2010 , generating spatial clustered frequency filtered identified pixel values of skin  2014 . The spatial-cluster module  2012  performs block  2506  in  FIG. 25 . In some implementations the spatial-cluster module  2012  includes fuzzy clustering, k-means clustering, expectation-maximization process, Ward&#39;s apparatus or seed point based clustering. 
     In some implementations, apparatus  2000  includes a frequency-filter module  2016  that applies a frequency filter to the spatial clustered frequency filtered identified pixel values of skin  2014 , which generates frequency filtered spatial clustered frequency filtered identified pixel values of skin  2018 . The frequency-filter module  2016  performs block  2508  in  FIG. 25 . In some implementations, the frequency-filter module  2016  is a one-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. Some implementations of frequency-filter module  2016  includes de-noising (e.g. smoothing of the data with a Gaussian filter). 
     The skin-pixel-identification module  2002 , the frequency-filter module  2008 , the spatial-cluster module  2012  and the frequency-filter module  2016  amplify temporal variations in the two or more images  1604 . 
     In some implementations, apparatus  2000  includes a temporal-variation module  2020  that determines temporal variation  2022  of the frequency filtered spatial clustered frequency filtered identified pixel values of skin  2018 . Thus, temporal variation  2022  represents temporal variation of the images  1604 . The temporal-variation module  2020  performs block  2510  in  FIG. 25 . 
       FIG. 21  is a block diagram of an apparatus  2100  to generate and present any one of a number of biological vital signs from amplified motion, according to an implementation. 
     In some implementations, apparatus  2100  includes a blood-flow-analyzer module  2102  that analyzes a temporal variation to generate a pattern of flow of blood  2104 . One example of the temporal variation is temporal variation  2022  in  FIG. 20 . In some implementations, the pattern flow of blood  2104  is generated from motion changes in the pixels and the temporal variation of color changes in the skin of the images  1604 . In some implementations, apparatus  2100  includes a blood-flow display module  2106  that displays the pattern of flow of blood  2104  for review by a healthcare worker. 
     In some implementations, apparatus  2100  includes a heartrate-analyzer module  2108  that analyzes the temporal variation to generate a heartrate  2110 . In some implementations, the heartrate  2110  is generated from the frequency spectrum of the temporal signal in a frequency range for heart beats, such as (0-10 Hertz). In some implementations, apparatus  2100  includes a heartrate display module  2112  that displays the heartrate  2110  for review by a healthcare worker. 
     In some implementations, apparatus  2100  includes a respiratory rate-analyzer module  2114  that analyzes the temporal variation to determine a respiratory rate  2116 . In some implementations, the respiratory rate  2116  is generated from the motion of the pixels in a frequency range for respiration (0-5 Hertz). In some implementations, apparatus  2100  includes respiratory rate display module  2118  that displays the respiratory rate  2116  for review by a healthcare worker. 
     In some implementations, apparatus  2100  includes a blood-pressure analyzer module  2120  that analyzes the temporal variation to a generate blood pressure  2122 . In some implementations, the blood-pressure analyzer module  2120  generates the blood pressure  2122  by analyzing the motion of the pixels and the color changes based on a clustering process and potentially temporal data. In some implementations, apparatus  2100  includes a blood pressure display module  2124  that displays the blood pressure  2122  for review by a healthcare worker. 
     In some implementations, apparatus  2100  includes an EKG analyzer module  2126  that analyzes the temporal variation to generate an EKG  2128 . In some implementations, apparatus  2100  includes an EKG display module  2130  that displays the EKG  2128  for review by a healthcare worker. 
     In some implementations, apparatus  2100  includes a pulse oximetry analyzer module  2132  that analyzes the temporal variation to generate pulse oximetry  2134 . In some implementations, the pulse oximetry analyzer module  2132  generates the pulse oximetry  2134  by analyzing the temporal color changes based in conjunction with the k-means clustering process and potentially temporal data. In some implementations, apparatus  2100  includes a pulse oximetry display module  2136  that displays the pulse oximetry  2134  for review by a healthcare worker. 
       FIG. 22  is a block diagram of an apparatus  2200  of variation amplification, according to an implementation. Apparatus  2200  analyzes the temporal and spatial variations in digital images of an animal subject in order to generate and communicate biological vital signs. 
     In some implementations, apparatus  2200  includes a skin-pixel-identification module  2002  that identifies pixel values  2006  that are representative of the skin in two or more images  1604 . The skin-pixel-identification module  2002  performs block  2502  in  FIG. 25 . Some implementations of the skin-pixel-identification module  2002  perform an automatic seed point based clustering process on the least two images  1604 . 
     In some implementations, apparatus  2200  includes a frequency-filter module  2008  that receives the identified pixel values  2006  that are representative of the skin and applies a frequency filter to the identified pixel values  2006 . The frequency-filter module  2008  performs block  2504  in  FIG. 25  to process the images  1604  in the frequency domain. Each of the images  1604  is Fourier transformed, multiplied with a filter function and then re-transformed into the spatial domain. Frequency filtering is based on the Fourier Transform. The operator takes an image  1604  and a filter function in the Fourier domain. The image  1604  is then multiplied with the filter function in a pixel-by-pixel fashion using the
 
 G ( k, 1)= F ( k, 1) H ( k, 1)  formula:
 
     where F(k,l) is the input image  1604  of identified pixel values  2006  in the Fourier domain, H(k,l) the filter function and G(k,l) is the filtered image  2010 . To obtain the resulting image in the spatial domain, G(k,l) is re-transformed using the inverse Fourier Transform. In some implementations, the frequency-filter module  2008  is a two-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. 
     In some implementations, apparatus  2200  includes a spatial-cluster module  2012  that applies spatial clustering to the frequency filtered identified pixel values of skin  2010 , generating spatial clustered frequency filtered identified pixel values of skin  2014 . The spatial-cluster module  2012  performs block  2506  in  FIG. 25 . In some implementations the spatial clustering includes fuzzy clustering, k-means clustering, expectation-maximization process, Ward&#39;s apparatus or seed point based clustering. 
     In some implementations, apparatus  2200  includes a frequency-filter module  2016  that applies a frequency filter to the spatial clustered frequency filtered identified pixel values of skin  2014 , which generates frequency filtered spatial clustered frequency filtered identified pixel values of skin  2018 . The frequency-filter module  2016  performs block  2508  in  FIG. 25  to generate a temporal variation  2022 . In some implementations, the frequency-filter module  2016  is a one-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. Some implementations of the frequency-filter module  2016  includes de-noising (e.g. smoothing of the data with a Gaussian filter). The skin-pixel-identification module  2002 , the frequency-filter module  2008 , the spatial-cluster module  2012  and the frequency-filter module  2016  amplify temporal variations in the two or more images  1604 . 
     The frequency-filter module  2016  is operably coupled to one of more modules in  FIG. 21  to generate and present any one or a number of biological vital signs from amplified motion in the temporal variation  2022 . 
       FIG. 23  is a block diagram of an apparatus  2300  of variation amplification, according to an implementation. Apparatus  2300  analyzes the temporal and spatial variations in digital images of an animal subject in order to generate and communicate biological vital signs. 
     In some implementations, apparatus  2300  includes a skin-pixel-identification module  2002  that identifies pixel values  2006  that are representative of the skin in two or more images  1604 . The skin-pixel-identification module  2002  performs block  2502  in  FIG. 27 . Some implementations of the skin-pixel-identification module  2002  perform an automatic seed point based clustering process on the least two images  1604 . In some implementations, apparatus  2300  includes a spatial bandpass filter module  2302  that applies a spatial bandpass filter to the identified pixel values  2006 , generating spatial bandpassed filtered identified pixel values of skin  2304 . In some implementations, the spatial bandpass filter module  2302  includes a two-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. The spatial bandpass filter module  2302  performs block  2702  in  FIG. 27 . 
     In some implementations, apparatus  2300  includes a spatial-cluster module  2012  that applies spatial clustering to the frequency filtered identified pixel values of skin  2010 , generating spatial clustered spatial bandpassed identified pixel values of skin  2306 . In some implementations the spatial clustering includes fuzzy clustering, k-means clustering, expectation-maximization process, Ward&#39;s apparatus or seed point based clustering. The spatial-cluster module  2012  performs block  2704  in  FIG. 27 . 
     In some implementations, apparatus  2300  includes a temporal bandpass filter module  2308  that applies a temporal bandpass filter to the spatial clustered spatial bandpass filtered identified pixel values of skin  2306 , generating temporal bandpass filtered spatial clustered spatial bandpass filtered identified pixel values of skin  2310 . In some implementations, the temporal bandpass filter is a one-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. The temporal bandpass filter module  2308  performs block  2706  in  FIG. 27 . 
     In some implementations, apparatus  2300  includes a temporal-variation module  2020  that determines temporal variation  2422  of the temporal bandpass filtered spatial clustered spatial bandpass filtered identified pixel values of skin  2310 . Thus, temporal variation  2422  represents temporal variation of the images  1604 . The temporal-variation module  2420  performs block  2708  of  FIG. 27 . The temporal-variation module  2420  is operably coupled to one or more modules in  FIG. 21  to generate and present any one of a number of biological vital signs from amplified motion in the temporal variation  2422 . 
       FIG. 24  is a block diagram of an apparatus  2400  of variation amplification, according to an implementation. 
     In some implementations, apparatus  2400  includes a pixel-examination-module  2402  that examines pixel values of two or more images  1604 , generating examined pixel values  2404 . The pixel-examination-module  2402  performs block  2802  in  FIG. 28 . 
     In some implementations, apparatus  2400  includes a temporal variation determiner module  2406  that determines a temporal variation  2408  of the examined pixel values  2404 . The temporal variation determiner module  2406  performs block  2804  in  FIG. 28 . 
     In some implementations, apparatus  2400  includes a signal-processing module  2410  that applies signal processing to the pixel value temporal variations  2408 , generating an amplified temporal variation  2422 . The signal-processing module  2410  performs block  2806  in  FIG. 28 . The signal processing amplifies the temporal variation  2408 , even when the temporal variation  2408  is small. In some implementations, the signal processing performed by signal-processing module  2410  is temporal bandpass filtering that analyzes frequencies over time. In some implementations, the signal processing performed by signal-processing module  2410  is spatial processing that removes noise. Apparatus  2400  amplifies only small temporal variations in the signal-processing module. 
     While apparatus  2400  can process large temporal variations, an advantage in apparatus  2400  is provided for small temporal variations. Therefore apparatus  2400  is most effective when the two or more images  1604  have small temporal variations between the two or more images  1604 . In some implementations, a vital sign is generated from the amplified temporal variations of the two or more images  1604  from the signal-processing module  2410 . 
     Vital Sign Amplification Method Implementations 
       FIG. 25-29  each use spatial and temporal signal processing to generate vital signs from a series of digital images. 
       FIG. 25  is a flowchart of a method  2500  of variation amplification, according to an implementation. Method  2500  analyzes the temporal and spatial variations in digital images of an animal subject in order to generate and communicate biological vital signs. 
     In some implementations, method  2500  includes identifying pixel values of two or more images that are representative of the skin, at block  2502 . Some implementations of identifying pixel values that are representative of the skin includes performing an automatic seed point based clustering process on the least two images. 
     In some implementations, method  2500  includes applying a frequency filter to the identified pixel values that are representative of the skin, at block  2504 . In some implementations, the frequency filter in block  2504  is a two-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. 
     In some implementations, method  2500  includes applying spatial clustering to the frequency filtered identified pixel values of skin, at block  2506 . In some implementations the spatial clustering includes fuzzy clustering, k-means clustering, expectation-maximization process, Ward&#39;s method or seed point based clustering. 
     In some implementations, method  2500  includes applying a frequency filter to the spatial clustered frequency filtered identified pixel values of skin, at block  2508 . In some implementations, the frequency filter in block  2508  is a one-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. Some implementations of applying a frequency filter at block  2508  include de-noising (e.g. smoothing of the data with a Gaussian filter). 
     Actions  2502 ,  2504 ,  2506  and  2508  amplify temporal variations in the two or more images. 
     In some implementations, method  2500  includes determining temporal variation of the frequency filtered spatial clustered frequency filtered identified pixel values of skin, at block  2510 . 
     In some implementations, method  2500  includes analyzing the temporal variation to generate a pattern of flow of blood, at block  2512 . In some implementations, the pattern flow of blood is generated from motion changes in the pixels and the temporal variation of color changes in the skin. In some implementations, method  2500  includes displaying the pattern of flow of blood for review by a healthcare worker, at block  2513 . 
     In some implementations, method  2500  includes analyzing the temporal variation to generate heartrate, at block  2514 . In some implementations, the heartrate is generated from the frequency spectrum of the temporal variation in a frequency range for heart beats, such as (0-10 Hertz). In some implementations, method  2500  includes displaying the heartrate for review by a healthcare worker, at block  2515 . 
     In some implementations, method  2500  includes analyzing the temporal variation to determine respiratory rate, at block  2516 . In some implementations, the respiratory rate is generated from the motion of the pixels in a frequency range for respiration (0-5 Hertz). In some implementations, method  2500  includes displaying the respiratory rate for review by a healthcare worker, at block  2517 . 
     In some implementations, method  2500  includes analyzing the temporal variation to generate blood pressure, at block  2518 . In some implementations, the blood pressure is generated by analyzing the motion of the pixels and the color changes based on the clustering process and potentially temporal data from the infrared sensor. In some implementations, method  2500  includes displaying the blood pressure for review by a healthcare worker, at block  2519 . 
     In some implementations, method  2500  includes analyzing the temporal variation to generate EKG, at block  2520 . In some implementations, method  2500  includes displaying the EKG for review by a healthcare worker, at block  2521 . 
     In some implementations, method  2500  includes analyzing the temporal variation to generate pulse oximetry, at block  2522 . In some implementations, the pulse oximetry is generated by analyzing the temporal color changes based in conjunction with the k-means clustering process and potentially temporal data from the infrared sensor. In some implementations, method  2500  includes displaying the pulse oximetry for review by a healthcare worker, at block  2523 . 
       FIG. 26  is a flowchart of a method of variation amplification, according to an implementation that does not include a separate action of determining a temporal variation. Method  2600  analyzes the temporal and spatial variations in digital images of an animal subject in order to generate and communicate biological vital signs. 
     In some implementations, method  2600  includes identifying pixel values of two or more images that are representative of the skin, at block  2502 . Some implementations of identifying pixel values that are representative of the skin includes performing an automatic seed point based clustering process on the least two images. 
     In some implementations, method  2600  includes applying a frequency filter to the identified pixel values that are representative of the skin, at block  2504 . In some implementations, the frequency filter in block  2504  is a two-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. 
     In some implementations, method  2600  includes applying spatial clustering to the frequency filtered identified pixel values of skin, at block  2506 . In some implementations the spatial clustering includes fuzzy clustering, k-means clustering, expectation-maximization process, Ward&#39;s method or seed point based clustering. 
     In some implementations, method  2600  includes applying a frequency filter to the spatial clustered frequency filtered identified pixel values of skin, at block  2508 , yielding a temporal variation. In some implementations, the frequency filter in block  2508  is a one-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. 
     In some implementations, method  2600  includes analyzing the temporal variation to generate a pattern of flow of blood, at block  2512 . In some implementations, the pattern flow of blood is generated from motion changes in the pixels and the temporal variation of color changes in the skin. In some implementations, method  2600  includes displaying the pattern of flow of blood for review by a healthcare worker, at block  2513 . 
     In some implementations, method  2600  includes analyzing the temporal variation to generate heartrate, at block  2514 . In some implementations, the heartrate is generated from the frequency spectrum of the temporal variation in a frequency range for heart beats, such as (0-10 Hertz). In some implementations, method  2600  includes displaying the heartrate for review by a healthcare worker, at block  2515 . 
     In some implementations, method  2600  includes analyzing the temporal variation to determine respiratory rate, at block  2516 . In some implementations, the respiratory rate is generated from the motion of the pixels in a frequency range for respiration (0-5 Hertz). In some implementations, method  2600  includes displaying the respiratory rate for review by a healthcare worker, at block  2517 . 
     In some implementations, method  2600  includes analyzing the temporal variation to generate blood pressure, at block  2518 . In some implementations, the blood pressure is generated by analyzing the motion of the pixels and the color changes based on the clustering process and potentially temporal data from the infrared sensor. In some implementations, method  2600  includes displaying the blood pressure for review by a healthcare worker, at block  2519 . 
     In some implementations, method  2600  includes analyzing the temporal variation to generate EKG, at block  2520 . In some implementations, method  2600  includes displaying the EKG for review by a healthcare worker, at block  2521 . 
     In some implementations, method  2600  includes analyzing the temporal variation to generate pulse oximetry, at block  2522 . In some implementations, the pulse oximetry is generated by analyzing the temporal color changes based in conjunction with the k-means clustering process and potentially temporal data from the infrared sensor. In some implementations, method  2600  includes displaying the pulse oximetry for review by a healthcare worker, at block  2523 . 
       FIG. 27  is a flowchart of a method  2700  of variation amplification from which to generate and communicate biological vital signs, according to an implementation. Method  2700  analyzes the temporal and spatial variations in digital images of an animal subject in order to generate and communicate the biological vital signs. 
     In some implementations, method  2700  includes identifying pixel values of two or more images that are representative of the skin, at block  2502 . Some implementations of identifying pixel values that are representative of the skin includes performing an automatic seed point based clustering process on the least two images. 
     In some implementations, method  2700  includes applying a spatial bandpass filter to the identified pixel values, at block  2702 . In some implementations, the spatial filter in block  2702  is a two-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. 
     In some implementations, method  2700  includes applying spatial clustering to the spatial bandpass filtered identified pixel values of skin, at block  2704 . In some implementations the spatial clustering includes fuzzy clustering, k-means clustering, expectation-maximization process, Ward&#39;s method or seed point based clustering. 
     In some implementations, method  2700  includes applying a temporal bandpass filter to the spatial clustered spatial bandpass filtered identified pixel values of skin, at block  2706 . In some implementations, the temporal bandpass filter in block  2706  is a one-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. 
     In some implementations, method  2700  includes determining temporal variation of the temporal bandpass filtered spatial clustered spatial bandpass filtered identified pixel values of skin, at block  2708 . 
     In some implementations, method  2700  includes analyzing the temporal variation to generate and visually display a pattern of flow of blood, at block  2512 . In some implementations, the pattern flow of blood is generated from motion changes in the pixels and the temporal variation of color changes in the skin. In some implementations, method  2700  includes displaying the pattern of flow of blood for review by a healthcare worker, at block  2513 . 
     In some implementations, method  2700  includes analyzing the temporal variation to generate heartrate, at block  2514 . In some implementations, the heartrate is generated from the frequency spectrum of the temporal variation in a frequency range for heart beats, such as (0-10 Hertz). In some implementations, method  2700  includes displaying the heartrate for review by a healthcare worker, at block  2515 . 
     In some implementations, method  2700  includes analyzing the temporal variation to determine respiratory rate, at block  2516 . In some implementations, the respiratory rate is generated from the motion of the pixels in a frequency range for respiration (0-5 Hertz). In some implementations, method  2700  includes displaying the respiratory rate for review by a healthcare worker, at block  2517 . 
     In some implementations, method  2700  includes analyzing the temporal variation to generate blood pressure, at block  2518 . In some implementations, the blood pressure is generated by analyzing the motion of the pixels and the color changes based on the clustering process and potentially temporal data from the infrared sensor. In some implementations, method  2700  includes displaying the blood pressure for review by a healthcare worker, at block  2519 . 
     In some implementations, method  2700  includes analyzing the temporal variation to generate EKG, at block  2520 . In some implementations, method  2700  includes displaying the EKG for review by a healthcare worker, at block  2521 . 
     In some implementations, method  2700  includes analyzing the temporal variation to generate pulse oximetry, at block  2522 . In some implementations, the pulse oximetry is generated by analyzing the temporal color changes based in conjunction with the k-means clustering process and potentially temporal data from the infrared sensor. In some implementations, method  2700  includes displaying the pulse oximetry for review by a healthcare worker, at block  2523 . 
       FIG. 28  is a flowchart of a method  2800  of variation amplification, according to an implementation. Method  2800  displays the temporal variations based on temporal variations in videos that are difficult or impossible to see with the naked eye. Method  2800  applies spatial decomposition to a video, and applies temporal filtering to the frames. The resulting signal is then amplified to reveal hidden information. Method  2800  can visualize flow of blood filling a face in the video and also amplify and reveal small motions, and other vital signs such as blood pressure, respiration, EKG and pulse. Method  2800  can execute in real time to show phenomena occurring at temporal frequencies selected by the operator. A combination of spatial and temporal processing of videos can amplify subtle variations that reveal important aspects of the world. Method  2800  considers a time series of color values at any spatial location (e.g., a pixel) and amplifies variation in a given temporal frequency band of interest. For example, method  2800  selects and then amplifies a band of temporal frequencies including plausible human heart rates. The amplification reveals the variation of redness as blood flows through the face. Lower spatial frequencies are temporally filtered (spatial pooling) to allow a subtle input signal to rise above the solid-state image transducer  328  and quantization noise. The temporal filtering approach not only amplifies color variation, but can also reveal low-amplitude motion. 
     Method  2800  can enhance the subtle motions around the chest of a breathing baby. Method  2800  mathematical analysis employs a linear approximation related to the brightness constancy assumption used in optical flow formulations. Method  2800  also derives the conditions under which the linear approximation holds. The derivation leads to a multiscale approach to magnify motion without feature tracking or motion estimation. Properties of a voxel of fluid are observed, such as pressure and velocity, which evolve over time. Method  2800  studies and amplifies the variation of pixel values over time, in a spatially-multiscale manner. The spatially-multiscale manner to motion magnification does not explicitly estimate motion, but rather exaggerates motion by amplifying temporal color changes at fixed positions. Method  2800  employs differential approximations that form the basis of optical flow processes. Method  2800  described herein employs localized spatial pooling and bandpass filtering to extract and reveal visually the signal corresponding to the pulse. The domain analysis allows amplification and visualization of the pulse signal at each location on the face. Asymmetry in facial blood flow can be a symptom of arterial problems. 
     Method  2800  described herein makes imperceptible motions visible using a multiscale approach. Method  2800  amplifies small motions, in one embodiment. Nearly invisible changes in a dynamic environment can be revealed through spatio-temporal processing of standard monocular video sequences. Moreover, for a range of amplification values that is suitable for various applications, explicit motion estimation is not required to amplify motion in natural videos. Method  2800  is well suited to small displacements and lower spatial frequencies. Single framework can amplify both spatial motion and purely temporal changes (e.g., a heart pulse) and can be adjusted to amplify particular temporal frequencies. A spatial decomposition module decomposes the input video into different spatial frequency bands, then applies the same temporal filter to the spatial frequency bands. The outputted filtered spatial bands are then amplified by an amplification factor, added back to the original signal by adders, and collapsed by a reconstruction module to generate the output video. The temporal filter and amplification factors can be tuned to support different applications. For example, the system can reveal unseen motions of a solid-state image transducer  328 , caused by the flipping mirror during a photo burst. 
     Method  2800  combines spatial and temporal processing to emphasize subtle temporal changes in a video. Method  2800  decomposes the video sequence into different spatial frequency bands. These bands might be magnified differently because (a) the bands might exhibit different signal-to-noise ratios or (b) the bands might contain spatial frequencies for which the linear approximation used in motion magnification does not hold. In the latter case, method  2800  reduces the amplification for these bands to suppress artifacts. When the goal of spatial processing is to increase temporal signal-to-noise ratio by pooling multiple pixels, the method spatially low-pass filters the frames of the video and downsamples the video frames for computational efficiency. In the general case, however, method  2800  computes a full Laplacian pyramid. 
     Method  2800  then performs temporal processing on each spatial band. Method  2800  considers the time series corresponding to the value of a pixel in a frequency band and applies a bandpass filter to extract the frequency bands of interest. As one example, method  2800  may select frequencies within the range of 0.4-4 Hz, corresponding to 24-240 beats per minute, if the operator wants to magnify a pulse. If method  2800  extracts the pulse rate, then method  2800  can employ a narrow frequency band around that value. The temporal processing is uniform for all spatial levels and for all pixels within each level. Method  2800  then multiplies the extracted bandpassed signal by a magnification factor .alpha. The magnification factor .alpha. can be specified by the operator, and can be attenuated automatically. Method  2800  adds the magnified signal to the original signal and collapses the spatial pyramid to obtain the final output. Since natural videos are spatially and temporally smooth, and since the filtering is performed uniformly over the pixels, the method implicitly maintains spatiotemporal coherency of the results. The motion magnification amplifies small motion without tracking motion. Temporal processing produces motion magnification, shown using an analysis that relies on the first-order Taylor series expansions common in optical flow analyses. 
     Method  2800  begins with a pixel-examination module in the microprocessor  302  of the non-touch biologic detectors  300 ,  400  or  300  examining pixel values of two or more images  1604  from the solid-state image transducer  328 , at block  2802 . 
     Method  2800  thereafter determines the temporal variation of the examined pixel values, at block  2804  by a temporal-variation module in the microprocessor  302 . 
     A signal-processing module in the microprocessor  302  applies signal processing to the pixel value temporal variations, at block  2806 . Signal processing amplifies the determined temporal variations, even when the temporal variations are small. Method  2800  amplifies only small temporal variations in the signal-processing module. While method  2800  can be applied to large temporal variations, an advantage in method  2800  is provided for small temporal variations. Therefore method  2800  is most effective when the input images  1604  have small temporal variations between the images  1604 . In some implementations, the signal processing at block  2806  is temporal bandpass filtering that analyzes frequencies over time. In some implementations, the signal processing at block  2806  is spatial processing that removes noise. 
     In some implementations, a vital sign is generated from the amplified temporal variations of the input images  1604  from the signal processor at block  2808 . Examples of generating a vital signal from a temporal variation include as in actions  2512 ,  2514 ,  2516 ,  2518 ,  2520  and  2522  in  FIGS. 25, 26 and 27 . 
       FIG. 29  is a flowchart of a method  2900  of variation amplification from which to generate and communicate biological vital signs, according to an implementation. Method  2900  analyzes the temporal and spatial variations in digital images of an animal subject in order to generate and communicate the biological vital signs. 
     In some implementations, method  2900  includes cropping at least two images to exclude areas that do not include a skin region, at block  2902 . For example, the excluded area can be a perimeter area around the center of each image, so that an outside border area of the image is excluded. In some implementations of cropping out the border, about 72% of the width and about 72% of the height of each image is cropped out, leaving only 7.8% of the original uncropped image, which eliminates about 11/12 of each image and reduces the amount of processing time for the remainder of the actions in this process by about 12-fold. This one action alone at block  2902  in method  2900  can reduce the processing time of plurality of images  330  in comparison to method  2700  from 4 minutes to 30 seconds, which is of significant difference to the health workers who used devices that implement method  2900 . In some implementations, the remaining area of the image after cropping in a square area and in other implementation the remaining area after cropping is a circular area. Depending upon the topography and shape of the area in the images that has the most pertinent portion of the imaged subject, different geometries and sizes are most beneficial. The action of cropping the images at block  2902  can be applied at the beginning of methods  2500 ,  2600 ,  2700  and  2800  in  FIGS. 25, 26, 27 and 28 , respectively. In other implementations of apparatus  1600 ,  1700 ,  1800 ,  1900 ,  2000 ,  2100 ,  2200 ,  2300  and  2400 , a cropper module that performs block  2902  is placed at the beginning of the modules to greatly decrease processing time of the apparatus. 
     In some implementations, method  2900  includes identifying pixel values of the at least two or more cropped images that are representative of the skin, at block  2904 . Some implementations of identifying pixel values that are representative of the skin include performing an automatic seed point based clustering process on the least two images. 
     In some implementations, method  2900  includes applying a spatial bandpass filter to the identified pixel values, at block  2702 . In some implementations, the spatial filter in block  2702  is a two-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. 
     In some implementations, method  2900  includes applying spatial clustering to the spatial bandpass filtered identified pixel values of skin, at block  2704 . In some implementations the spatial clustering includes fuzzy clustering, k-means clustering, expectation-maximization process, Ward&#39;s method or seed point based clustering. 
     In some implementations, method  2900  includes applying a temporal bandpass filter to the spatial clustered spatial bandpass filtered identified pixel values of skin, at block  2706 . In some implementations, the temporal bandpass filter in block  2706  is a one-dimensional spatial Fourier Transform, a high pass filter, a low pass filter, a bandpass filter or a weighted bandpass filter. 
     In some implementations, method  2900  includes determining temporal variation of the temporal bandpass filtered spatial clustered spatial bandpass filtered identified pixel values of skin, at block  2708 . 
     In some implementations, method  2900  includes analyzing the temporal variation to generate and visually display a pattern of flow of blood, at block  2512 . In some implementations, the pattern flow of blood is generated from motion changes in the pixels and the temporal variation of color changes in the skin. In some implementations, method  2900  includes displaying the pattern of flow of blood for review by a healthcare worker, at block  2513 . 
     In some implementations, method  2900  includes analyzing the temporal variation to generate heartrate, at block  2514 . In some implementations, the heartrate is generated from the frequency spectrum of the temporal variation in a frequency range for heart beats, such as (0-10 Hertz). In some implementations, method  2900  includes displaying the heartrate for review by a healthcare worker, at block  2515 . 
     In some implementations, method  2900  includes analyzing the temporal variation to determine respiratory rate, at block  2516 . In some implementations, the respiratory rate is generated from the motion of the pixels in a frequency range for respiration (0-5 Hertz). In some implementations, method  2900  includes displaying the respiratory rate for review by a healthcare worker, at block  2517 . 
     In some implementations, method  2900  includes analyzing the temporal variation to generate blood pressure, at block  2518 . In some implementations, the blood pressure is generated by analyzing the motion of the pixels and the color changes based on the clustering process and potentially temporal data from the infrared sensor. In some implementations, method  2900  includes displaying the blood pressure for review by a healthcare worker, at block  2519 . 
     In some implementations, method  2900  includes analyzing the temporal variation to generate EKG, at block  2520 . In some implementations, method  2900  includes displaying the EKG for review by a healthcare worker, at block  2521 . 
     In some implementations, method  2900  includes analyzing the temporal variation to generate pulse oximetry, at block  2522 . In some implementations, the pulse oximetry is generated by analyzing the temporal color changes based in conjunction with the k-means clustering process and potentially temporal data from the infrared sensor. In some implementations, method  2900  includes displaying the pulse oximetry for review by a healthcare worker, at block  2523 . 
     Non-Touch Cubic Temperature Estimation Method Implementations 
       FIG. 30  is a flowchart of a method  3000  to estimate a body core temperature from an external source point in reference to a cubic relationship, according to an implementation. 
     Method  3000  includes receiving from a non-touch electromagnetic sensor a numerical representation of electromagnetic energy of the external source point of a subject, at block  3002 . 
     Method  3000  also includes estimating the body core temperature of the subject from the numerical representation of the electromagnetic energy of the external source point, a representation of an ambient air temperature reading, a representation of a calibration difference, and a representation of a bias in consideration of the temperature sensing mode, at block  3004 . The estimating at block  3004  is based on a cubic relationship representing three thermal ranges between the body core temperature and the numerical representation of the electromagnetic energy of the external source point. The cubic relationship includes a coefficient representative of different relationships between the external source point and the body core temperature in the three thermal ranges. 
     A cubic relationship for all ranges of ambient temperatures provides best results because a linear or a quadratic relationship provide inaccurate estimates of body temperature, yet a quartic relationship, a quintic relationship, sextic relationship, a septic relationship or an octic relationship provide estimates along a highly irregular curve that is far too wavy or twisting with relatively sharp deviations from one ambient temperature to another ambient temperature. 
     Method  3000  also includes displaying the body core temperature, at block  3006 . 
       FIG. 31  is a flowchart of a method  3100  to estimate a body core temperature from an external source point and other measurements in reference to a cubic relationship, according to an implementation; 
     Method  3100  includes receiving from a non-touch electromagnetic sensor a numerical representation of electromagnetic energy of the external source point of a subject, at block  3002 . 
     Method  3100  also includes estimating the body core temperature of the subject from the numerical representation of the electromagnetic energy of the external source point, a representation of an ambient air temperature reading, a representation of a calibration difference, and a representation of a bias in consideration of the temperature sensing mode, at block  3102 . The estimating at block  3104  is based on a cubic relationship representing three thermal ranges between the body core temperature and the numerical representation of the electromagnetic energy of the external source point. The cubic relationship includes a coefficient representative of different relationships between the external source point and the body core temperature in the three thermal ranges, wherein the cubic relationship is:
 
 T   B   =AT   Skin   3   +BT   Skin   1   +CT   Skin   +D−E ( T   Ambient −75), T   Ambient   &lt;T   1  or  T   Ambient   &gt;T   2  and  T   B   =AT   Skin   3   +BT   Skin   2   +CT   Skin   +D,T   1   &lt;T   Ambient   &lt;T   2  
         where:
           T B  is the body core temperature   T skin  is the numerical representation of the electromagnetic energy of the external source point   A is 0.0002299688   B is −0.0464237524   C is 3.05944877   D is 31.36205   E is 0.135   T ambient  is the ambient air temperature   T 1  and T 2  are boundaries between the three thermal ranges   T 1  and T 2  are selected from a group of pairs of ambient temperatures consisting of 67° F. and 82° F.; 87° F. and 95° F.; and 86° F. and 101° F.   
               

     Method  3100  also includes displaying the body core temperature, at block  3006 . 
     In some implementations, methods  2500 - 3100  are implemented as a sequence of instructions which, when executed by a microprocessor  302  in  FIG. 3-12 , microprocessor  3504  In  FIG. 35 , main processor  3202  in  FIG. 32  or processing unit  3304  in  FIG. 33 , cause the processor to perform the respective method. In other implementations, methods  2500 - 3100  are implemented as a computer-accessible medium having computer executable instructions capable of directing a microprocessor, such as microprocessor  302  in  FIG. 3-12 , microprocessor  3504  in  FIG. 35 , main processor  3202  in  FIG. 32  or processing unit  3304  in  FIG. 33 , to perform the respective method. In different implementations, the medium is a magnetic medium, an electronic medium, or an optical medium. 
     Hardware and Operating Environments 
       FIG. 32  is a block diagram of a hand-held device  3200 , according to an implementation. The hand-held device  3200  may also have the capability to allow voice communication. Depending on the functionality provided by the hand-held device  3200 , the hand-held device  3200  may be referred to as a data messaging device, a two-way pager, a cellular telephone with data messaging capabilities, a wireless Internet appliance, or a data communication device (with or without telephony capabilities). 
     The hand-held device  3200  includes a number of modules such as a main processor  3202  that controls the overall operation of the hand-held device  3200 . Communication functions, including data and voice communications, are performed through a communication subsystem  3204 . The communication subsystem  3204  receives messages from and sends messages to wireless networks  3205 . In other implementations of the hand-held device  3200 , the communication subsystem  3204  can be configured in accordance with the Global System for Mobile Communication (GSM), General Packet Radio Services (GPRS), Enhanced Data GSM Environment (EDGE), Universal Mobile Telecommunications Service (UMTS), data-centric wireless networks, voice-centric wireless networks, and dual-mode networks that can support both voice and data communications over the same physical base stations. Combined dual-mode networks include, but are not limited to, Code Division Multiple Access (CDMA) or CDMA2000 networks, GSM/GPRS networks (as mentioned above), and future third-generation (3G) networks like EDGE and UMTS. Some other examples of data-centric networks include Mobitex™ and DataTAC™ network communication systems. Examples of other voice-centric data networks include Personal Communication Systems (PCS) networks like GSM and Time Division Multiple Access (TDMA) systems. 
     The wireless link connecting the communication subsystem  3204  with the wireless network  3205  represents one or more different Radio Frequency (RF) channels. With newer network protocols, these channels are capable of supporting both circuit switched voice communications and packet switched data communications. 
     The main processor  3202  also interacts with additional subsystems such as a Random Access Memory (RAM)  3206 , a flash memory  3208 , a display  3210 , an auxiliary input/output (I/O) subsystem  3212 , a data port  3214 , a keyboard  3216 , a speaker  3218 , a microphone  3220 , short-range communications subsystem  3222  and other device subsystems  3224 . In some implementations, the flash memory  3208  includes a hybrid femtocell/Wi-Fi protocol stack  3209 . The stack  3209  supports authentication and authorization between the hand-held device  3200  into a shared Wi-Fi network and both a 3G and 4G mobile networks. 
     Some of the subsystems of the hand-held device  3200  perform communication-related functions, whereas other subsystems may provide “resident” or on-device functions. By way of example, the display  3210  and the keyboard  3216  may be used for both communication-related functions, such as entering a text message for transmission over the wireless network  3205 , and device-resident functions such as a calculator or task list. 
     The hand-held device  3200  can transmit and receive communication signals over the wireless network  3205  after required network registration or activation procedures have been completed. Network access is associated with a subscriber or user of the hand-held device  3200 . To identify a subscriber, the hand-held device  3200  requires a SIM/RUIM card  3226  (i.e. Subscriber Identity Module or a Removable User Identity Module) to be inserted into a SIM/RUIM interface  3228  in order to communicate with a network. The SIM card or RUIM  3226  is one type of a conventional “smart card” that can be used to identify a subscriber of the hand-held device  3200  and to personalize the hand-held device  3200 , among other things. Without the SIM card  3226 , the hand-held device  3200  is not fully operational for communication with the wireless network  3205 . By inserting the SIM card/RUIM  3226  into the SIM/RUIM interface  3228 , a subscriber can access all subscribed services. Services may include: web browsing and messaging such as e-mail, voice mail, Short Message Service (SMS), and Multimedia Messaging Services (MMS). More advanced services may include: point of sale, field service and sales force automation. The SIM card/RUIM  3226  includes a processor and memory for storing information. Once the SIM card/RUIM  3226  is inserted into the SIM/RUIM interface  3228 , the SIM is coupled to the main processor  3202 . In order to identify the subscriber, the SIM card/RUIM  3226  can include some user parameters such as an International Mobile Subscriber Identity (IMSI). An advantage of using the SIM card/RUIM  3226  is that a subscriber is not necessarily bound by any single physical mobile device. The SIM card/RUIM  3226  may store additional subscriber information for the hand-held device  3200  as well, including datebook (or calendar) information and recent call information. Alternatively, user identification information can also be programmed into the flash memory  3208 . 
     The hand-held device  3200  is a battery-powered device and includes a battery interface  3232  for receiving one or more rechargeable batteries  3230 . In one or more implementations, the battery  3230  can be a smart battery with an embedded microprocessor. The battery interface  3232  is coupled to a regulator  3233 , which assists the battery  3230  in providing power V+ to the hand-held device  3200 . Although current technology makes use of a battery, future technologies such as micro fuel cells may provide the power to the hand-held device  3200 . 
     The hand-held device  3200  also includes an operating system  3234  and modules  3236  to  3249  which are described in more detail below. The operating system  3234  and the modules  3236  to  3249  that are executed by the main processor  3202  are typically stored in a persistent nonvolatile medium such as the flash memory  3208 , which may alternatively be a read-only memory (ROM) or similar storage element (not shown). Those skilled in the art will appreciate that portions of the operating system  3234  and the modules  3236  to  3249 , such as specific device applications, or parts thereof, may be temporarily loaded into a volatile store such as the RAM  3206 . Other modules can also be included. 
     The subset of modules  3236  that control basic device operations, including data and voice communication applications, will normally be installed on the hand-held device  3200  during its manufacture. Other modules include a message application  3238  that can be any suitable module that allows a user of the hand-held device  3200  to transmit and receive electronic messages. Various alternatives exist for the message application  3238  as is well known to those skilled in the art. Messages that have been sent or received by the user are typically stored in the flash memory  3208  of the hand-held device  3200  or some other suitable storage element in the hand-held device  3200 . In one or more implementations, some of the sent and received messages may be stored remotely from the hand-held device  3200  such as in a data store of an associated host system with which the hand-held device  3200  communicates. 
     The modules can further include a device state module  3240 , a Personal Information Manager (PIM)  3242 , and other suitable modules (not shown). The device state module  3240  provides persistence, i.e. the device state module  3240  ensures that important device data is stored in persistent memory, such as the flash memory  3208 , so that the data is not lost when the hand-held device  3200  is turned off or loses power. 
     The PIM  3242  includes functionality for organizing and managing data items of interest to the user, such as, but not limited to, e-mail, contacts, calendar events, voice mails, appointments, and task items. A PIM application has the ability to transmit and receive data items via the wireless network  3205 . PIM data items may be seamlessly integrated, synchronized, and updated via the wireless network  3205  with the hand-held device  3200  subscriber&#39;s corresponding data items stored and/or associated with a host computer system. This functionality creates a mirrored host computer on the hand-held device  3200  with respect to such items. This can be particularly advantageous when the host computer system is the hand-held device  3200  subscriber&#39;s office computer system. 
     The hand-held device  3200  also includes a connect module  3244 , and an IT policy module  3246 . The connect module  3244  implements the communication protocols that are required for the hand-held device  3200  to communicate with the wireless infrastructure and any host system, such as an enterprise system, with which the hand-held device  3200  is authorized to interface. Examples of a wireless infrastructure and an enterprise system are given in  FIGS. 32 and 33 , which are described in more detail below. 
     The connect module  3244  includes a set of APIs that can be integrated with the hand-held device  3200  to allow the hand-held device  3200  to use any number of services associated with the enterprise system. The connect module  3244  allows the hand-held device  3200  to establish an end-to-end secure, authenticated communication pipe with the host system. A subset of applications for which access is provided by the connect module  3244  can be used to pass IT policy commands from the host system to the hand-held device  3200 . This can be done in a wireless or wired manner. These instructions can then be passed to the IT policy module  3246  to modify the configuration of the hand-held device  3200 . Alternatively, in some cases, the IT policy update can also be done over a wired connection. 
     The IT policy module  3246  receives IT policy data that encodes the IT policy. The IT policy module  3246  then ensures that the IT policy data is authenticated by the hand-held device  3200 . The IT policy data can then be stored in the flash memory  3206  in its native form. After the IT policy data is stored, a global notification can be sent by the IT policy module  3246  to all of the applications residing on the hand-held device  3200 . Applications for which the IT policy may be applicable then respond by reading the IT policy data to look for IT policy rules that are applicable. 
     The IT policy module  3246  can include a parser  3247 , which can be used by the applications to read the IT policy rules. In some cases, another module or application can provide the parser. Grouped IT policy rules, described in more detail below, are retrieved as byte streams, which are then sent (recursively) into the parser to determine the values of each IT policy rule defined within the grouped IT policy rule. In one or more implementations, the IT policy module  3246  can determine which applications are affected by the IT policy data and transmit a notification to only those applications. In either of these cases, for applications that are not being executed by the main processor  3202  at the time of the notification, the applications can call the parser or the IT policy module  3246  when the applications are executed to determine if there are any relevant IT policy rules in the newly received IT policy data. 
     All applications that support rules in the IT Policy are coded to know the type of data to expect. For example, the value that is set for the “WEP User Name” IT policy rule is known to be a string; therefore the value in the IT policy data that corresponds to this rule is interpreted as a string. As another example, the setting for the “Set Maximum Password Attempts” IT policy rule is known to be an integer, and therefore the value in the IT policy data that corresponds to this rule is interpreted as such. 
     After the IT policy rules have been applied to the applicable applications or configuration files, the IT policy module  3246  sends an acknowledgement back to the host system to indicate that the IT policy data was received and successfully applied. 
     The programs  3237  can also include a temporal-variation-amplifier  3248  and a vital sign generator  3249 . In some implementations, the temporal-variation-amplifier  3248  includes a skin-pixel-identifier  1602 , a frequency-filter  1606 , a regional facial clusterial module  1608  and a frequency filter  1610  as in  FIGS. 16 and 17 . In some implementations, the temporal-variation-amplifier  3248  includes a skin-pixel-identifier  1602 , a spatial bandpass-filter  1802 , regional facial clusterial module  1608  and a temporal bandpass filter  1804  as in  FIG. 18 . In some implementations, the temporal-variation-amplifier  3248  includes a pixel-examiner  1902 , a temporal variation determiner  1906  and signal processor  1908  as in  FIG. 19 . In some implementations, the temporal-variation-amplifier  3248  includes a skin-pixel-identification module  2002 , a frequency-filter module  2008 , spatial-cluster module  2012  and a frequency filter module  2016  as in  FIGS. 20 and 21 . In some implementations, the temporal-variation-amplifier module  2002 , a spatial bandpass filter module  2302 , a spatial-cluster module  2012  and a temporal bandpass filter module  2306  as in  FIG. 23 . In some implementations, the temporal-variation-amplifier  3248  includes a pixel-examination-module  2402 , a temporal variation determiner module  2406  and a signal processing module  2410  as in  FIG. 24 . The solid-state image transducer  328  captures images  330  and the vital sign generator  3249  generates the vital sign(s)  1616  that is displayed by display  3210  or transmitted by communication subsystem  3204  or short-range communications subsystem  3222 , enunciated by speaker  3218  or stored by flash memory  3208 . 
     Other types of modules can also be installed on the hand-held device  3200 . These modules can be third party modules, which are added after the manufacture of the hand-held device  3200 . Examples of third party applications include games, calculators, utilities, etc. 
     The additional applications can be loaded onto the hand-held device  3200  through at least one of the wireless network  3205 , the auxiliary I/O subsystem  3212 , the data port  3214 , the short-range communications subsystem  3222 , or any other suitable device subsystem  3224 . This flexibility in application installation increases the functionality of the hand-held device  3200  and may provide enhanced on-device functions, communication-related functions, or both. For example, secure communication applications may enable electronic commerce functions and other such financial transactions to be performed using the hand-held device  3200 . 
     The data port  3214  enables a subscriber to set preferences through an external device or module and extends the capabilities of the hand-held device  3200  by providing for information or module downloads to the hand-held device  3200  other than through a wireless communication network. The alternate download path may, for example, be used to load an encryption key onto the hand-held device  3200  through a direct and thus reliable and trusted connection to provide secure device communication. 
     The data port  3214  can be any suitable port that enables data communication between the hand-held device  3200  and another computing device. The data port  3214  can be a serial or a parallel port. In some instances, the data port  3214  can be a USB port that includes data lines for data transfer and a supply line that can provide a charging current to charge the battery  3230  of the hand-held device  3200 . 
     The short-range communications subsystem  3222  provides for communication between the hand-held device  3200  and different systems or devices, without the use of the wireless network  3205 . For example, the subsystem  3222  may include an infrared device and associated circuits and modules for short-range communication. Examples of short-range communication standards include standards developed by the Infrared Data Association (IrDA), Bluetooth, and the 802.11 family of standards developed by IEEE. 
     Bluetooth is a wireless technology standard for exchanging data over short distances (using short-wavelength radio transmissions in the ISM band from 2400-2480 MHz) from fixed and mobile devices, creating personal area networks (PANs) with high levels of security. Created by telecom vendor Ericsson in  2894 , Bluetooth was originally conceived as a wireless alternative to RS-232 data cables. Blutooth can connect several devices, overcoming problems of synchronization. Bluetooth operates in the range of 2400-2483.5 MHz (including guard bands), which is in the globally unlicensed Industrial, Scientific and Medical (ISM) 2.4 GHz short-range radio frequency band. Bluetooth uses a radio technology called frequency-hopping spread spectrum. The transmitted data is divided into packets and each packet is transmitted on one of the 79 designated Bluetooth channels. Each channel has a bandwidth of 1 MHz. The first channel starts at 2402 MHz and continues up to 2480 MHz in 1 MHz steps. The first channel usually performs  1600  hops per second, with Adaptive Frequency-Hopping (AFH) enabled. Originally Gaussian frequency-shift keying (GFSK) modulation was the only modulation scheme available; subsequently, since the introduction of Bluetooth 2.0+EDR, π/4-DQPSK and 8DPSK modulation may also be used between compatible devices. Devices functioning with GFSK are said to be operating in basic rate (BR) mode where an instantaneous data rate of 1 Mbit/s is possible. The term Enhanced Data Rate (EDR) is used to describe π/4-DPSK and 8DPSK schemes, each giving 2 and 3 Mbit/s respectively. The combination of these (BR and EDR) modes in Bluetooth radio technology is classified as a “BR/EDR radio”. Bluetooth is a packet-based protocol with a master-slave structure. One master may communicate with up to 7 slaves in a piconet; all devices share the master&#39;s clock. Packet exchange is based on the basic clock, defined by the master, which ticks at 312.5 μs intervals. Two clock ticks make up a slot of 625 μs; two slots make up a slot pair of 1250 μs. In the simple case of single-slot packets the master transmits in even slots and receives in odd slots; the slave, conversely, receives in even slots and transmits in odd slots. Packets may be 1, 3 or 5 slots long but in all cases the master transmit will begin in even slots and the slave transmit in odd slots. A master Bluetooth device can communicate with a maximum of seven devices in a piconet (an ad-hoc computer network using Bluetooth technology), though not all devices reach this maximum. The devices can switch roles, by agreement, and the slave can become the master (for example, a headset initiating a connection to a phone will necessarily begin as master, as initiator of the connection; but may subsequently prefer to be slave). The Bluetooth Core Specification provides for the connection of two or more piconets to form a scatternet, in which certain devices simultaneously play the master role in one piconet and the slave role in another. At any given time, data can be transferred between the master and one other device (except for the little-used broadcast mode. The master chooses which slave device to address; typically, the master switches rapidly from one device to another in a round-robin fashion. Since the master chooses which slave to address, whereas a slave is (in theory) supposed to listen in each receive slot, being a master is a lighter burden than being a slave. Being a master of seven slaves is possible; being a slave of more than one master is difficult. Many of the services offered over Bluetooth can expose private data or allow the connecting party to control the Bluetooth device. For security reasons it is necessary to be able to recognize specific devices and thus enable control over which devices are allowed to connect to a given Bluetooth device. At the same time, it is useful for Bluetooth devices to be able to establish a connection without user intervention (for example, as soon as the Bluetooth devices of each other are in range). To resolve this conflict, Bluetooth uses a process called bonding, and a bond is created through a process called pairing. The pairing process is triggered either by a specific request from a user to create a bond (for example, the user explicitly requests to “Add a Bluetooth device”), or the pairing process is triggered automatically when connecting to a service where (for the first time) the identity of a device is required for security purposes. These two cases are referred to as dedicated bonding and general bonding respectively. Pairing often involves some level of user interaction; this user interaction is the basis for confirming the identity of the devices. Once pairing successfully completes, a bond will have been formed between the two devices, enabling those two devices to connect to each other in the future without requiring the pairing process in order to confirm the identity of the devices. When desired, the bonding relationship can later be removed by the user. Secure Simple Pairing (SSP): This is required by Bluetooth v2.1, although a Bluetooth v2.1 device may only use legacy pairing to interoperate with a v2.0 or earlier device. Secure Simple Pairing uses a form of public key cryptography, and some types can help protect against man in the middle, or MITM attacks. SSP has the following characteristics: Just works: As implied by the name, this method just works. No user interaction is required; however, a device may prompt the user to confirm the pairing process. This method is typically used by headsets with very limited 10 capabilities, and is more secure than the fixed PIN mechanism which is typically used for legacy pairing by this set of limited devices. This method provides no man in the middle (MITM) protection. Numeric comparison: If both devices have a display and at least one can accept a binary Yes/No user input, both devices may use Numeric Comparison. This method displays a 6-digit numeric code on each device. The user should compare the numbers to ensure that the numbers are identical. If the comparison succeeds, the user(s) should confirm pairing on the device(s) that can accept an input. This method provides MITM protection, assuming the user confirms on both devices and actually performs the comparison properly. Passkey Entry: This method may be used between a device with a display and a device with numeric keypad entry (such as a keyboard), or two devices with numeric keypad entry. In the first case, the display is used to show a 6-digit numeric code to the user, who then enters the code on the keypad. In the second case, the user of each device enters the same 6-digit number. Both of these cases provide MITM protection. Out of band (OOB): This method uses an external means of communication, such as Near Field Communication (NFC) to exchange some information used in the pairing process. Pairing is completed using the Bluetooth radio, but requires information from the OOB mechanism. This provides only the level of MITM protection that is present in the OOB mechanism. SSP is considered simple for the following reasons: In most cases, SSP does not require a user to generate a passkey. For use-cases not requiring MITM protection, user interaction can be eliminated. For numeric comparison, MITM protection can be achieved with a simple equality comparison by the user. Using OOB with NFC enables pairing when devices simply get close, rather than requiring a lengthy discovery process. 
     In use, a received signal such as a text message, an e-mail message, or web page download will be processed by the communication subsystem  3204  and input to the main processor  3202 . The main processor  3202  will then process the received signal for output to the display  3210  or alternatively to the auxiliary I/O subsystem  3212 . A subscriber may also compose data items, such as e-mail messages, for example, using the keyboard  3216  in conjunction with the display  3210  and possibly the auxiliary I/O subsystem  3212 . The auxiliary subsystem  3212  may include devices such as: a touch screen, mouse, track ball, infrared fingerprint detector, or a roller wheel with dynamic button pressing capability. The keyboard  3216  is preferably an alphanumeric keyboard and/or telephone-type keypad. However, other types of keyboards may also be used. A composed item may be transmitted over the wireless network  3205  through the communication subsystem  3204 . 
     For voice communications, the overall operation of the hand-held device  3200  is substantially similar, except that the received signals are output to the speaker  3218 , and signals for transmission are generated by the microphone  3220 . Alternative voice or audio I/O subsystems, such as a voice message recording subsystem, can also be implemented on the hand-held device  3200 . Although voice or audio signal output is accomplished primarily through the speaker  3218 , the display  3210  can also be used to provide additional information such as the identity of a calling party, duration of a voice call, or other voice call related information. 
       FIG. 33  is a block diagram of a hardware and operating environment  3300  in which different implementations can be practiced. The description of  FIG. 33  provides an overview of computer hardware and a suitable computing environment in conjunction with which some implementations can be implemented. Implementations are described in terms of a computer executing computer-executable instructions. However, some implementations can be implemented entirely in computer hardware in which the computer-executable instructions are implemented in read-only memory. Some implementations can also be implemented in client/server computing environments where remote devices that perform tasks are linked through a communications network. Program modules can be located in both local and remote memory storage devices in a distributed computing environment. 
       FIG. 33  illustrates an example of a computer environment  3300  useful in the context of the environment of  FIG. 3-18 , in accordance with an implementation. The computer environment  3300  includes a computation resource  3302  capable of implementing the processes described herein. It will be appreciated that other devices can alternatively used that include more modules, or fewer modules, than those illustrated in  FIG. 33 . 
     The illustrated operating environment  3300  is only one example of a suitable operating environment, and the example described with reference to  FIG. 33  is not intended to suggest any limitation as to the scope of use or functionality of the implementations of this disclosure. Other well-known computing systems, environments, and/or configurations can be suitable for implementation and/or application of the subject matter disclosed herein. 
     The computation resource  3302  includes one or more processors or processing units  3304 , a system memory  3306 , and a bus  3308  that couples various system modules including the system memory  3306  to processing unit  3304  and other elements in the environment  3300 . The bus  3308  represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port and a processor or local bus using any of a variety of bus architectures, and can be compatible with SCSI (small computer system interconnect), or other conventional bus architectures and protocols. 
     The system memory  3306  includes nonvolatile read-only memory (ROM)  3310  and random access memory (RAM)  3312 , which can or can not include volatile memory elements. A basic input/output system (BIOS)  3314 , containing the elementary routines that help to transfer information between elements within computation resource  3302  and with external items, typically invoked into operating memory during start-up, is stored in ROM  3310 . 
     The computation resource  3302  further can include a non-volatile read/write memory  3316 , represented in  FIG. 33  as a hard disk drive, coupled to bus  3308  via a data media interface  3317  (e.g., a SCSI, ATA, or other type of interface); a magnetic disk drive (not shown) for reading from, and/or writing to, a removable magnetic disk  3320  and an optical disk drive (not shown) for reading from, and/or writing to, a removable optical disk  3326  such as a CD, DVD, or other optical media. 
     The non-volatile read/write memory  3316  and associated computer-readable media provide nonvolatile storage of computer-readable instructions, data structures, program modules and other data for the computation resource  3302 . Although the exemplary environment  3300  is described herein as employing a non-volatile read/write memory  3316 , a removable magnetic disk  3320  and a removable optical disk  3326 , it will be appreciated by those skilled in the art that other types of computer-readable media which can store data that is accessible by a computer, such as magnetic cassettes, FLASH memory cards, random access memories (RAMs), read only memories (ROM), and the like, can also be used in the exemplary operating environment. 
     A number of program modules can be stored via the non-volatile read/write memory  3316 , magnetic disk  3320 , optical disk  3326 , ROM  3310 , or RAM  3312 , including an operating system  3330 , one or more application programs  3332 , program modules  3334  and program data  3336 . Examples of computer operating systems conventionally employed include the NUCLEUS® operating system, the LINUX® operating system, and others, for example, providing capability for supporting application programs  3332  using, for example, code modules written in the C++® computer programming language. The application programs  3332  and/or the program modules  3334  can also include a temporal-variation-amplifier (as shown in  3248  in  FIG. 32 ) and a vital sign generator (as shown in  3249  in  FIG. 33 ). In some implementations, the temporal-variation-amplifier  3248  in the application programs  3332  and/or the program modules  3334  includes a skin-pixel-identifier  1602 , a frequency-filter  1606 , regional facial clusterial module  1608  and a frequency filter  1610  as in  FIGS. 16 and 17 . In some implementations, the temporal-variation-amplifier  3248  in application programs  3332  and/or the program modules  3334  includes a skin-pixel-identifier  1602 , a spatial bandpass-filter  1802 , regional facial clusterial module  1608  and a temporal bandpass filter  1804  as in  FIG. 18 . In some implementations, the temporal-variation-amplifier  3248  in the application programs  3332  and/or the program modules  3334  includes a pixel-examiner  1902 , a temporal variation determiner  1906  and signal processor  1908  as in  FIG. 19 . In some implementations, the temporal-variation-amplifier  3248  in the application programs  3332  and/or the program modules  3334  includes a skin-pixel-identification module  2002 , a frequency-filter module  2008 , spatial-cluster module  2012  and a frequency filter module  2016  as in  FIGS. 20 and 21 . In some implementations, the temporal-variation-amplifier  3248  in the application programs  3332  and/or the program modules  3334  includes a skin-pixel-identification module  2002 , a spatial bandpass filter module  2302 , a spatial-cluster module  2012  and a temporal bandpass filter module  2306  as in  FIG. 23 . In some implementations, the temporal-variation-amplifier  3248  in the application programs  3332  and/or the program modules  3334  includes a pixel-examination-module  2402 , a temporal variation determiner module  2406  and a signal processing module  2410  as in  FIG. 24 . The solid-state image transducer  328  captures images  330  that are processed by the temporal-variation-amplifier  3248  and the vital sign generator  3249  to generate the vital sign(s)  1616  that is displayed by display  3350  or transmitted by computation resource  3302 , enunciated by a speaker or stored in program data  3336 . 
     A user can enter commands and information into computation resource  3302  through input devices such as input media  3338  (e.g., keyboard/keypad, tactile input or pointing device, mouse, foot-operated switching apparatus, joystick, touchscreen or touchpad, microphone, antenna etc.). Such input devices  3338  are coupled to the processing unit  3304  through a conventional input/output interface  3342  that is, in turn, coupled to the system bus. Display  3350  or other type of display device is also coupled to the system bus  3308  via an interface, such as a video adapter  3352 . 
     The computation resource  3302  can include capability for operating in a networked environment using logical connections to one or more remote computers, such as a remote computer  3360 . The remote computer  3360  can be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computation resource  3302 . In a networked environment, program modules depicted relative to the computation resource  3302 , or portions thereof, can be stored in a remote memory storage device such as can be associated with the remote computer  3360 . By way of example, remote application programs  3362  reside on a memory device of the remote computer  3360 . The logical connections represented in  FIG. 33  can include interface capabilities, e.g., such as interface capabilities in  FIG. 14 , a storage area network (SAN, not illustrated in  FIG. 33 ), local area network (LAN)  3372  and/or a wide area network (WAN)  3374 , but can also include other networks. 
     Such networking environments are commonplace in modern computer systems, and in association with intranets and the Internet. In certain implementations, the computation resource  3302  executes an Internet Web browser program (which can optionally be integrated into the operating system  3330 ), such as the “Internet Explorer®” Web browser manufactured and distributed by the Microsoft Corporation of Redmond, Wash. 
     When used in a LAN-coupled environment, the computation resource  3302  communicates with or through the local area network  3372  via a network interface or adapter  3376  and typically includes interfaces, such as a modem  3378 , or other apparatus, for establishing communications with or through the WAN  3374 , such as the Internet. The modem  3378 , which can be internal or external, is coupled to the system bus  3308  via a serial port interface. 
     In a networked environment, program modules depicted relative to the computation resource  3302 , or portions thereof, can be stored in remote memory apparatus. It will be appreciated that the network connections shown are exemplary, and other means of establishing a communications link between various computer systems and elements can be used. 
     A user of a computer can operate in a networked environment using logical connections to one or more remote computers, such as a remote computer  3360 , which can be a personal computer, a server, a router, a network PC, a peer device or other common network node. Typically, a remote computer  3360  includes many or all of the elements described above relative to the computer  3300  of  FIG. 33 . 
     The computation resource  3302  typically includes at least some form of computer-readable media. Computer-readable media can be any available media that can be accessed by the computation resource  3302 . By way of example, and not limitation, computer-readable media can comprise computer storage media and communication media. 
     Computer storage media include volatile and nonvolatile, removable and non-removable media, implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules or other data. The term “computer storage media” includes, but is not limited to, RAM, ROM, EEPROM, FLASH memory or other memory technology, CD, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other media which can be used to store computer-intelligible information and which can be accessed by the computation resource  3302 . 
     Communication media typically embodies computer-readable instructions, data structures, program modules or other data, represented via, and determinable from, a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal in a fashion amenable to computer interpretation. 
     By way of example, and not limitation, communication media include wired media, such as wired network or direct-wired connections, and wireless media, such as acoustic, RF, infrared and other wireless media. The scope of the term computer-readable media includes combinations of any of the above. 
       FIG. 34  is a representation of display  3400  that is presented on the display device of apparatus in  FIGS. 3-14 and 35-39 , according to an implementation. 
     Some implementations of display  3400  include a representation of three detection modes  3402 , a first detection mode being detection and display of surface temperature, a second detection mode being detection and display of body temperature and a third detection mode being detection and display of room temperature. 
     Some implementations of display  3400  include a representation of Celsius  3404  that is activated when the apparatus is in Celsius mode. 
     Some implementations of display  3400  include a representation of a sensed temperature  3406 . 
     Some implementations of display  3400  include a representation of Fahrenheit  3408  that is activated when the apparatus is in Fahrenheit mode. 
     Some implementations of display  3400  include a representation of a mode  3410  of site temperature sensing, a first site mode being detection of an axillary surface temperature, a second site mode being detection of an oral temperature, a third site mode being detection of a rectal temperature and a fourth site mode being detection of a core temperature. 
     Some implementations of display  3400  include a representation of a temperature traffic light  3412 , in which a green traffic light indicates that the temperature  320  is good; an amber traffic light indicates that the temperature  320  is low; and a red traffic light indicates that the temperature  320  is high. 
     Some implementations of display  3400  include a representation of a probe mode  3414  that is activated when the sensed temperature  3406  is from a contact sensor. 
     Some implementations of display  3400  include a representation of the current time/date  3416  of the apparatus. 
       FIG. 35-39  are schematics of the electronic components of a non-touch thermometer  3500  having a digital IR sensor.  FIG. 35  is a portion of the schematic of the non-touch thermometer  3500  having a digital IR sensor, according to an implementation. As discussed above in regards to  FIG. 4  and  FIG. 3 , thermal isolation of the digital IR sensor is an important feature. In a second circuit board  3501 , a digital IR sensor  308  is thermally isolated from the heat of the microprocessor  3504  (shown in  FIG. 36 ) through a first digital interface  3502 . The digital IR sensor  308  is not mounted on the same circuit board  3505  as the microprocessor  3504  (shown in  FIG. 36 ) which reduces heat transfer from a first circuit board  3505  to the digital IR sensor  308 . The non-touch thermometer  3500  also includes a second circuit board  3501 , the second circuit board  3501  including a second digital interface  3512 , the second digital interface  3512  being operably coupled to the first digital interface  3502  and a digital infrared sensor  308  being operably coupled to the second digital interface  3512 , the digital infrared sensor  308  having ports that provide only digital readout. The microprocessor  3504  (shown in  FIG. 36 ) is operable to receive from the ports that provide only digital readout a digital signal that is representative of an infrared signal generated by the digital infrared sensor  308  and the microprocessor  3504  (shown in  FIG. 36 ) is operable to determine a temperature from the digital signal that is representative of the infrared signal. The first circuit board  3505  includes all of the components in  FIG. 35 ,  FIG. 36  and  FIG. 37  other than the second circuit board  3501 , the digital IR sensor  308  and the second digital interface  3512 . 
       FIG. 36  is a portion of the schematic of the non-touch thermometer  3500  having the digital IR sensor, according to an implementation. A non-touch thermometer  3500  includes a first circuit board  3505 , the first circuit board  3505  including the microprocessor  3504 . 
       FIG. 37  is a portion of the schematic of the non-touch thermometer  3500  having the digital IR sensor, according to an implementation. The first circuit board  3505  includes a display device that is operably coupled to the microprocessor  3504  through a display interface  3508 . 
       FIG. 38  is a portion of the schematic of the non-touch thermometer  3500  having the digital IR sensor, according to an implementation. Circuit  3500  includes a battery  3506  that is operably coupled to the microprocessor  3504 , a single button  3510  that is operably coupled to the microprocessor  3504 . 
       FIG. 37  is a portion of the schematic of the non-touch thermometer  3500  having the digital IR sensor, according to an implementation. 
     The non-touch thermometer further includes a housing, and where the battery  304  is fixedly attached to the housing. The non-touch thermometer where an exterior portion of the housing further includes a magnet. 
     In some implementations, the microprocessor  302 , microprocessor  604 , microprocessor  3504  in  FIG. 35 , main processor  3202  in  FIG. 32  or processing unit  3304  in  FIG. 33  that do not use the digital infrared sensor  308  can be a digital signal processor (DSP) that is specialized for signal processing such as the Texas Instruments® C6000 series DSPs, the Freescale® MSC81xx family. 
     In some implementations, the microprocessor  604 , microprocessor  3504  in  FIG. 35 , main processor  3202  in  FIG. 32  or processing unit  3304  in  FIG. 33  can be a graphics processing unit GPU that use a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer, such as the Nvidia® GeForce 8 series. 
     In some implementations, the microprocessor  302 , microprocessor  604 , microprocessor  3504  in  FIG. 35 , main processor  3202  in  FIG. 32  or processing unit  3304  in  FIG. 33  can be field-programmable gate array (FPGA) that is an integrated circuit designed to be configured by a customer or a designer after manufacturing according to a hardware description language (HDL) using programmable logic components called “logic blocks”, and a hierarchy of reconfigurable interconnects that allow the blocks to be “wired together” that are changeable logic gates that can be inter-wired in different configurations that perform analog functions and/or digital functions. Logic blocks can be configured to perform complex combinational functions, or merely simple logic gates such as AND and XOR. In most FPGAs, the logic blocks also include memory elements, which may be simple flip-flops or more complete blocks of memory. 
       FIG. 40  is a block diagram of a solid-state image transducer  4000 , according to an implementation. The solid-state image transducer  4000  includes a great number of photoelectric elements, a.sub.1.sub.1, a.sub.2.sub.1, . . . , a.sub.mn, in the minute segment form, transfer gates TG1, TG2, . . . , TGn responsive to a control pulse V.sub.φP for transferring the charges stored on the individual photoelectric elements as an image signal to vertical shift registers VS1, VS2, . . . , VSn, and a horizontal shift register HS for transferring the image signal from the vertical shift registers VSs, through a buffer amplifier 2d to an outlet 2e. After the one-frame image signal is stored, the image signal is transferred to vertical shift register by the pulse V.sub.φP and the contents of the vertical shift registers VSs are transferred upward line by line in response to a series of control pulses V.sub.φV1, V.sub.φV2. During the time interval between the successive two vertical transfer control pulses, the horizontal shift register HS responsive to a series of control pulses V.sub.φH1, V.sub.φH2 transfers the contents of the horizontal shift registers HSs in each line row by row to the right as viewed in  FIG. 40 . As a result, the one-frame image signal is formed by reading out the outputs of the individual photoelectric elements in such order. 
       FIG. 41  is a block diagram of the communication subsystem  338 , according to an implementation. The communication subsystem  338  includes a receiver  4100 , a transmitter  4102 , as well as associated components such as one or more embedded or internal antenna elements  4104  and  4106 , Local Oscillators (LOs)  4108 , and a processing module such as a Digital Signal Processor (DSP)  4110 . The particular implementation of the communication subsystem  338  is dependent upon communication protocols of a wireless network  4105  with which the mobile device is intended to operate. Thus, it should be understood that the implementation illustrated in  FIG. 41  serves only as one example. Examples of the hand-held medical-data capture-device  104  include mobile device  3200 , non-touch biologic detector in  FIG. 3-5 , apparatus that estimates a body core temperature  4 - 10 , apparatus of variation amplification  FIGS. 36-20 and 22-24  and non-touch thermometer  3500 . Examples of the wireless network  4105  include network  3205  in  FIG. 32   
     Signals received by the antenna  4104  through the wireless network  4105  are input to the receiver  4100 , which may perform such common receiver functions as signal amplification, frequency down conversion, filtering, channel selection, and analog-to-digital (A/D) conversion. A/D conversion of a received signal allows more complex communication functions such as demodulation and decoding to be performed in the DSP  4110 . In a similar manner, signals to be transmitted are processed, including modulation and encoding, by the DSP  4110 . These DSP-processed signals are input to the transmitter  4102  for digital-to-analog (D/A) conversion, frequency up conversion, filtering, amplification and transmission over the wireless network  4105  via the antenna  4106 . The DSP  4110  not only processes communication signals, but also provides for receiver and transmitter control. For example, the gains applied to communication signals in the receiver  4100  and the transmitter  4102  may be adaptively controlled through automatic gain control algorithms implemented in the DSP  4110 . 
     The wireless link between the hand-held medical-data capture-device  104  and the wireless network  4105  can contain one or more different channels, typically different RF channels, and associated protocols used between the hand-held medical-data capture-device  104  and the wireless network  4105 . An RF channel is a limited resource that must be conserved, typically due to limits in overall bandwidth and limited battery power of the hand-held medical-data capture-device  104 . 
     When the hand-held medical-data capture-device  104  is fully operational, the transmitter  4102  is typically keyed or turned on only when it is transmitting to the wireless network  4105  and is otherwise turned off to conserve resources. Similarly, the receiver  4100  is periodically turned off to conserve power until the receiver  4100  is needed to receive signals or information (if at all) during designated time periods. 
     The PMR  103  is received by the communication subsystem  338  from the main processor  3202  at the DSP  4110  and then transmitted to the wireless network  4105  through the antenna  4104  of the receiver  4100 . 
     A non-touch biologic detector or thermometer that senses temperature through a digital infrared sensor, and transmits the temperature to an electronic medical record system. A technical effect of the apparatus and methods disclosed herein electronic transmission of a body core temperature that is estimated from signals from the non-touch electromagnetic sensor to a heterogeneous electronic medical record system. Another technical effect of the apparatus and methods disclosed herein is generating a temporal variation of images from which a vital sign can be transmitted to a heterogeneous electronic medical record system. Although specific implementations are illustrated and described herein, it will be appreciated by those of ordinary skill in the art that any arrangement which is generated to achieve the same purpose may be substituted for the specific implementations shown. This application is intended to cover any adaptations or variations. 
     In particular, one of skill in the art will readily appreciate that the names of the methods and apparatus are not intended to limit implementations. Furthermore, additional methods and apparatus can be added to the modules, functions can be rearranged among the modules, and new modules to correspond to future enhancements and physical devices used in implementations can be introduced without departing from the scope of implementations. One of skill in the art will readily recognize that implementations are applicable to future non-touch temperature sensing devices, different temperature measuring sites on humans or animals and new display devices. 
     The terminology used in this application meant to include all temperature sensors, processors and operator environments and alternate technologies which provide the same functionality as described herein.