Patent Abstract:
A disturbance detection, predictive analysis, and handling system and method is provided. The system and method may assist in identifying when, how, and what actions should be taken and by whom to prevent, reduce, or eliminate disturbances. The system and method may do so by monitoring or sensing activity in real time, performing predictive analytics on the data, and communicating the outcome to achieve a desired result. The system and method may include sensors provided within a sensor unit in communication with a hub. The sensor unit and hub may communicate over a personal area network. The system and method may also include a server and analytics engine to aid in the identification of disturbance and/or damage. The system and method may also use predictive algorithms and historic disruption data to enhance the accuracy of its prediction of disturbance and/or damage.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of U.S. Provisional Patent application No. 61/973,587 entitled “Disturbance Detection, Predictive Analysis, and Handling System” filed Apr. 1, 2014, which is hereby incorporated by reference. 
     
    
     FIELD 
       [0002]    The present inventions relate to the field of detection and alert systems. The present inventions more specifically relate to the field of detection, predictive analysis, and handling of a disturbance or potential disturbance. 
       BACKGROUND 
       [0003]    The reduction of noise disturbances in various medical, commercial, and residential settings is of significant importance. For example, the importance of reducing noise disturbances in hospitals and improving patient sleep experience has been well documented in medical journals, which have demonstrated a correlation between sleep experience and medical outcome. According to hospital survey results, such as the HCAHPS survey, the lowest score nationally is in response to how quiet the area around the patient&#39;s room was during the night. Not only does this noise and disturbance affect the health of the patient, but also Medicare uses the results of these surveys to determine the amount of money a hospital is to be reimbursed for patients using Medicare. If the hospital&#39;s results are below the national average, they receive a lower payment, while survey results above the national average result in a higher payment. 
         [0004]    While a hospital/medical/healthcare facility is described for purposes of example, in addition to medical or healthcare facilities, the hospitality industry also has a significant need to reduce disturbances to guests and, in particular, to guest sleep experiences. A common complaint is noise that results in the disturbance of guests in neighboring rooms, which can lead to a poor guest experience and subsequently a loss of business. 
         [0005]    Likewise, multi-family residential facilities have a need to reduce disturbances of tenants/occupants for similar reasons. A means of predicting damage in the management of a facility is also of particular importance. 
         [0006]    Accordingly, what is needed is a system which can detect and predict a disturbance, such as to a sleep experience, or detect and predict damage to a facility, and to identify when, how, and what actions should be taken to prevent, reduce, or eliminate these disturbances or damage. 
       SUMMARY 
       [0007]    Accordingly, a disturbance detection, predictive analysis, and handling system is provided. The system described herein may assist in identifying when, how, and what actions should be taken and by whom to prevent, reduce, or eliminate disturbances. The system described herein may do so by monitoring or sensing activity in real time, performing predictive analytics on the data, and communicating the outcome to achieve a desired result. The system may include sensors and networks to aid in the identification of disturbance and/or damage. The system may also use predictive algorithms and historic disruption data to enhance the accuracy of its prediction of disturbance and/or damage. 
         [0008]    In one illustrative example of implementation of the system, a system may be used in a hospital or assisted living facility for predicting disturbances to patient sleep experience through the use of various sensors. The system may identify when and what actions should be taken, and by whom, to prevent, reduce or eliminate these disturbances. In an alternative example of embodiments, the system may predict disturbances in the hospitality industry to guest sleep experiences. The system in this example may identify when, how, and what actions should be taken and by whom to prevent, reduce, or eliminate these disturbances. In another alternative example of embodiments, the system may predict disturbances to tenant/occupant sleep experience in a multi-family residential setting. The system may identify when, how, and what actions should be taken and by whom to prevent, reduce or eliminate these disturbances. In a further example of embodiments in facility management, the system may predict damage to a facility and identify when, how, and what actions should be taken and by whom to prevent, reduce, or eliminate this damage. 
         [0009]    The system may use one or more sensors to read environmental data such as ambient temperature, noise, humidity, or light. The sensors may be provided in a sensor unit. The sensor unit may be placed in strategic positions throughout a building. For example, in patient rooms at a hospital. The sensor units may report to a central hub, which sends information to a server for processing and prediction. The server may identify or anticipate potential disruptions from the sensor data and alert personnel. Further analytics may be provided using a data analytics engine. The data analytics engine may create a data pattern or signature from the data and compare it with historical data signatures. Using these and other techniques, the data processing/analytics engine may provide a predictive report to users for disruption tracking and anticipation. 
         [0010]    A disturbance detection, predictive analysis, and handling system is disclosed, including a sensor unit having a sound sensor and one or more network communication devices, the sensor unit having a processor programmed to transmit sensor data using the network communication device; a hub having one or more network communication devices, a processor, and data storage component, wherein the processor is programmed to accept and transmit the sensor unit data; a server having a network communication device, processor, and database, wherein the processor is programmed to analyze the sensor unit data; and a reporting device having a reporting software, the reporting device having a network communication device; wherein the network communication devices of the sensor unit, hub, and reporting device are selected from the group of Zigbee, Bluetooth, Wi-Fi, mobile broadband modem, or Ethernet. 
         [0011]    A disturbance detection, predictive analysis, and handling method is disclosed, the method comprising: providing one or more sensors including a sound sensor within a first networked device in a first location; using one or more sensors provided within the first networked device to obtain a first group of sensor reads over a period of time; determining a first example value from the first group of sensor reads using the first networked device; transmitting the first example value from the first networked device to a first hub device; providing one or more sensors including a sound sensor within a second networked device in a second location; using one or more sensors provided within the second networked device to obtain a second group of sensor reads over a period of time; determining a second example value from the second group of sensor reads using the second networked device; transmitting the second example value from the second networked device to the first hub device; transmitting the first example value and second example value to a server; analyzing the first example value and second example value against one or more thresholds; transmitting an alert signal to an alert device; and saving the first example value and second example value to create historical data. 
         [0012]    A disturbance detection, predictive analysis, and handling system is disclosed, the system comprising: a plurality of sensor devices that transmit data to one or more sensor hubs, each sensor device having: a sound sensor, a unique identifier, a personal area network identification, a personal area network communication device, and a microprocessor programmed to determine a highest sound value over a period of time; one or more sensor hubs that accept data from the plurality of sensor devices to a server, each sensor hub having: a sound sensor, a unique identifier, a personal area network identification, a personal area network communication device, a network communication device, and a microprocessor programmed to determine a highest sound value over a period of time and to send sensor values from the plurality of sensor devices to a server using the network communication device; a server which accepts data from the one or more sensor hubs, the server having: a network communication device, a database, and a processor programmed to execute an alert algorithm; a dashboard which accepts data from the server, the dashboard having: a network communication device, and a user interface displaying sensor data; and a data analytics engine that accepts data from the server, the data analytics engine having: a network communication device, a database containing historic sensor data and user-provided data, and a processor programmed to execute analysis algorithms. 
         [0013]    These and other features and advantages of devices, systems, and methods according to this invention are described in, or are apparent from, the following detailed descriptions of various examples of embodiments. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0014]    Various examples of embodiments of the systems, devices, and methods according to this invention will be described in detail, with reference to the following figures, wherein: 
           [0015]      FIG. 1  is a representation of an embodiment of a system implemented in a building. 
           [0016]      FIG. 2  is a logical flow diagram between the parts of an example embodiment of the system. 
           [0017]      FIG. 3  is an example embodiment of a process executed by the system. 
           [0018]      FIG. 4  is an example relationship between an example server and example data analytics engine of the disclosed system. 
           [0019]      FIG. 5  is an example logic flow of an example server for use with the system disclosed. 
           [0020]      FIG. 6  is an example logic flow of an example data analytics engine of the system disclosed. 
           [0021]      FIG. 7  is an example implementation of the system in a hospital environment. 
           [0022]      FIG. 8  is a block schematic representation of an example sensor unit. 
           [0023]      FIG. 9  is a block schematic representation of an example alert fob. 
       
    
    
       [0024]    It should be understood that the drawings are not necessarily to scale. In certain instances, details that are not necessary to the understanding of the invention or render other details difficult to perceive may have been omitted. It should be understood, of course, that the invention is not necessarily limited to the particular embodiments illustrated herein. 
       DETAILED DESCRIPTION 
       [0025]    The system  101  may be comprised in various embodiments of five primary (non-limiting) components: sensor unit  103 , hub  105 , server  107 , data analytics engine  109 , and dashboard  111 . Additional features such as an alert device  113 , input device  115 , experiential data  157 , and prediction reports  159  may likewise be included. It should be understood that while the figures may disclose a certain number of units, hubs, servers, etc., any appropriate number of components may be used. For example, many sensor units may be used with one hub, many hubs may be used with one remote server, many dashboards may be used with multiple sensor units and hubs, etc. It should also be understood certain components may be removed or combined—for example, removal of the data analytics engine or provision of the data analytics engine within the server. 
         [0026]      FIG. 1  shows an example scenario where a system  101  according to one or more examples of embodiments may be used. In  FIG. 1 , sensor units  103  are provided throughout a building, a hub  105  is provided in a central location in the building, and a server  107  and data analytics engine  109  are provided externally. Devices displaying the dashboard  111  or alert devices  113  may also be provided. As will be further described herein, the sensor units  103  may use sensors to detect various attributes about the surrounding environment, transmit those attributes to the hub  105 , which will transmit the acquired data to a server  107  which may relate data to an alert device  113  or dashboard  111 . The data may also be sent to a data analytics engine  109 . 
         [0027]      FIG. 2  shows some of the contents of the primary components of the system and how information may progress from sensor unit  103  to hub  105  to server  107  and onward to a data analytics engine  109 , dashboard  111 , alert device  113 , or prediction report  159 . It should be understood each component may include a processor which is programmed to complete the component&#39;s respective steps described herein. 
         [0028]    As shown, the sensor unit  103  is provided with a number of components, including sensors  117 / 119 / 121 , a unique identifier  123 , and network communication  125 . The unit  103  also has sensors, which may include (but is not limited to) a sound sensor  117 , a temperature and/or humidity sensor  119 , and a light sensor  121 . It should be understood that these three sensor types are example sensors for use in the system, and different types of sensors may be added (or the example sensors removed) depending on the application of the sensor system. The sensors  117 / 119 / 121  each may use mechanisms to measure designated information from the environment. The sensors may gather this information with varied frequency. For example, the sensor unit may be configured to require the sound sensor  117  to obtain a sound sample every second, whereas the temperature and/or humidity sensor  119  may be configured to measure samples much less frequently, for example once every ten seconds. In various embodiments, the sound sensor  117  may measure the sound in the area surrounding the device once every millisecond and take the highest read data each second. In other words, each sensor unit may measure the decibel level a thousand times per second and identify the highest decibel for that second. The particular frequency of information obtained and how it may be obtained may be stored in sensor unit  103  firmware as programmed by the manufacturer or altered and updated through the network, for example, to reflect a security update or user preferences. 
         [0029]    The composition of the sensor unit  103  is shown in further detail in  FIG. 8 . As shown, a sensor unit  103  may be comprised of externally visible components such as light-emitting diodes (LEDs)  165 , a power supply  173 , reset  169 , and housing (not shown). In various embodiments, the sensor unit may have a unitary body (housing) with a pronged outlet engagement, whereby the unit sits flush with a wall and is plugged into a standard outlet. In another embodiment, the sensor unit may include a power supply adaptable with a universal serial bus (USB) plug (mini, micro, standard, etc.). In other embodiments, the power mechanism may include an external transformer with a cord, or a plug and corresponding external jack. In another embodiment, the sensor unit  103  may be powered by a lithium ion or other type of battery. Combinations of the foregoing and other appropriate known charging mechanisms may also be used. Likewise, the sensor unit  103  may include a voltage regulation circuit to supply proper power to the unit components. 
         [0030]    In various embodiments, the sensor unit  103  may include a temperature/humidity sensor  119 , a light sensor  121 , and a sound sensor  117 . The sound sensor may include a variety of other components to allow for accurate sound readings, including a microphone input, amplifier, amplifier voltage reference, ideal diode envelope detector, and subtractor/rail-to-rail. These are non-limiting examples of components to allow for accurate and useful sound measurement. In various embodiments, the sensor unit  103  may further include one or more network communication mechanisms. For example, the unit may include a radio frequency network module embedded on the microcontroller  161  as well as a Bluetooth module  163 . Both of these modules may be used, for example, to generate a mesh wireless transaction network such as a personal area connection. The sensor unit may include various information storage devices, for example, flash memory  171 . In various embodiments, the sensor unit  103  may also include one or more programming headers  167  and one or more serial port chips  175  that are used in connection with a controller for (for example) bi-directional communication. 
         [0031]    In order to send the acquired ambient information to another device, as shown above, the sensor unit  103  may include a network communication device  125  such as, but not limited to, a radio communication device (Zigbee, Bluetooth, Wi-Fi, mobile broadband modem) or wired communication device such as Ethernet. In various embodiments, the sensor unit  103  may be configured to transmit raw data to a hub  105 . In the non-limiting example above, the sound sensor  117  on the sensor unit  103  measures the surrounding decibel level one thousand times per second, identifying and keeping the highest decibel for that second. The sensor unit may, for example, be configured to send information to the hub  105  every two seconds; therefore, the sensor unit may create and send a data packet including two readings (the highest sampled level in each of those two seconds) to the hub  105 . 
         [0032]    Using a processor and onboard memory (such as flash memory), the sensor unit  103  may gather the sensor data, provide a time stamp associated with the time the sensor data was obtained, provide a unique identification code  123 , and send the data, time, and associated code  123  in one or more data packets to the sensor hub  105  by way of the network communication device  125 . In various embodiments, the sensor unit(s)  103  may be configured to only connect with one particular hub. This may be facilitated by using only a particular radio band, password, or other appropriate means such as a personal area network identification (PAN ID)  133  which may correspondingly be saved on the sensor unit  103 . This PAN ID  133  may correspond with a PAN ID provided on the hub  131 . The sensor unit  103  may also have lights such as LEDs to indicate its connectivity status. 
         [0033]    The sensor hub  105  may have one or more network communication devices  127 , a unique device identifier (unique ID)  129 , and a PAN ID  131 . The sensor hub  105  may also include sensor components  118 / 120 / 122 . The network communication devices  127  may include one or more types of radio communication devices (Zigbee, Bluetooth, Wi-Fi, mobile broadband modem) or wired communication device such as Ethernet. For example, the sensor hub may have a Zigbee, Ethernet, and Wi-Fi network communication device. The sensor hub  105  may also have memory including RAM or flash memory. Likewise, the sensor hub  105  may include a programmable microcontroller, such as a Raspberry Pi. The sensor hub  105  may be configured to use its sensors, which may include (but are not limited to) a sound sensor  118 , temperature and/or humidity sensor  120 , and light sensor  122  in much the same way as a sensor unit  103 . As such, looking at  FIG. 1 , the sensor hub  105  may be used to also obtain sensor data in a central location in the building. 
         [0034]    Much of the logic of the system may take place at the server  107  level. In various embodiments, the server may have one or more of the following: a wireless or wired network communication device  135  such as an Ethernet connection, a processor  137 , storage  139  (for example, a database), configuration parameters  141 , and a programmed alert algorithm  143 , among other programmed components, which may include a restful opportunities calculator. For example, the server may be a remote cloud server having storage and processing hardware, such as high frequency Intel Xeon processors and SSD storage. The server may be of any suitable type, for example, Linux (including but not limited to Red Hat and SUSE) or Windows. 
         [0035]    Similarly, the data analytics engine  109  may include one or more of the following: a network communication device  145 , processor  147 , storage  149 , configuration  151 , and analysis algorithms  153 . The processor and storage may be any suitable programmable hardware or data structure, for example, a relational database. 
         [0036]    The remote server  107  and data analytics engine  109  may report results to a variety of output devices such as, but not limited to, an alert device  113 , user dashboard  111  or predictive reports  159 . The alert device  113  may be a variety of devices, such as a cell phone, tablet, or other suitable device capable of receiving texts, emails, or other digital message means. The user dashboard  111  may be a website or application which may provide a graphical user interface displaying historical and predictive data, as well as alerts. Likewise, prediction reports  159  may be sent through the dashboard  111 , email, or other suitable means. 
         [0037]    The system may also accept input devices  115 , which may be for configuration  155 , to clear alerts, or input experiential data  157 . This input device  115  may also house the dashboard  111 . In various embodiments, the dashboard  111  itself may allow for inputs. 
         [0038]      FIG. 3  describes the steps of an example simplified data flow from one component to the next. As shown in step S 201  of  FIG. 3 , the sensor unit will query the sensors and receive the reads. Once these reads are received, the sensor unit  103  may determine an example value of each group of reads over a time period, and the example values for each time period may be sent to the hub S 203 . In various embodiments, the network communication device  125  may transmit the data packet to the hub  105  by way of other sensor units using a shortest-path algorithm (i.e. a “daisy chain” transmission). This transmission may append the prior module data to the next module&#39;s data, and so on until the hub is reached. In this way, the sensor unit module may act as a router, receiving another unit&#39;s data packet, appending its own packet, and routing the combined information to the appropriate next module. 
         [0039]    The sensor hub  105  may receive the unit data, identifier  123 , and timestamps from multiple devices. The hub  105  may aggregate the sensor data S 205  (which may come from multiple sensor devices) and send the data in step S 207  to a server  107 . The hub  105  may have multiple data communication mechanisms  127 . For example, the hub  105  may accept data from the sensor unit(s)  103  using a Bluetooth or Zigbee connection, while it may send data to a server  107  using a wireless card, Ethernet, or other suitable means. The hub  105  may be configured to only accept transmissions from certain sensor unit(s). For example, each sensor unit  103  may use a PAN ID that correlates with the PAN ID of the hub  105  in order to ensure transmission only between the hub  105  and its associated units  103 . Such a configuration can allow for multiple networks in buildings across multiple floors, for example, without confusing the location of the sensor unit(s)  103 . The configuration of the sensor unit  103  and hub  105  may be over a relatively large area, for example, a 75-foot indoor distance between the sensor unit  103  and hub  105 . 
         [0040]    In various embodiments, a personal area network communication (for example, between the multiple sensor devices and hub(s)) may be distinct from the communication between the hub  105  and the server  107 . This is because the server  107  may be a remote cloud server. In this embodiment, the server  107  may not be provided on a personal area network with the hub  105  and sensor units  103 . The server  107  then may be accessed by the hub  105  using an internet connection. 
         [0041]    The hub  105  may be set up by a manufacturer to perform in a particular way. The hub  105  may also be updated with customizations to aid in the functionality of the system or a manufacturer may push firmware (or other) updates through the system to the hub  105  or sensor unit(s)  103 . Optimization may involve the frequency in which a hub  105  sends information to a server  107 . For example, the hub  105  may send information to the server  107 , which may include the sensor information and identifying information, along with a hub identifier, once every fifteen seconds. The hub may or may not store some of the data it is sent by the sensor unit  103 . For example, in the event of an internet service outage preventing uploading to the server  107 , the hub  105  may continue to receive and store several hours of data and send the data to the server  107  when the outage is fixed. In order to facilitate this storage, the hub may have both volatile and non-volatile memory, such as both random access memory (RAM) and a secure digital memory card (SD card). 
         [0042]    The hub  105  may be configured in various ways to upload bundled sensor information to the server  107  (step S 207 ). The hub  105  may be configured to allow two-way communication between the server  107  and the hub  105 . Various security measures may be required for this implementation. For example, a user may create a virtual private network (VPN) to communicate between the hub  105  and server  107  in a secure manner. The hub  105  may use a network; the hub  105  may be configured with the wireless name and password. This may include a guest wireless network; in that instance the hub  105  may be configured with a login script to get the password. In various embodiments, the hub  105  may use a cellular hotspot having login data. The hub  105  in these instances is programmed sufficiently to establish an internet connection. Configuration may be made, for example, by plugging the hub  105  into a laptop or other computer using a universal serial bus (USB) or Ethernet connection. In another embodiment, the hub  105  may create an ad hoc network for configuration. By connecting to the hub  105 , the user may be provided with means to provide the hub with configuration details such as a network name and password. Once it is configured, the hub  105  may have lights such as light-emitting diodes (LEDs) to indicate certain information such as hub  105  connectivity status. The lights may be programmed to blink, change color, or any other means to create message codes. 
         [0043]    The server  107  may receive the sensor data provided by the hub  105  at regular intervals, for example, every fifteen seconds. At a general level, the server  107  may be configured to analyze the data packet contents sent by the hub  105  in step S 209  and also, in step S 211 , send the data to a user-facing dashboard  111  or data analytics engine  109 . The server may determine whether to alert, or otherwise send a report in step S 211 . 
         [0044]      FIG. 4  shows an example relationship between the server  107  and data analytics engine  109 . The server  107  accepts customization  155  and other inputs from a user (for example, setting a sensor unit  103  to be in an unoccupied room or alter the sensor unit&#39;s environmental context settings). The server  107  may optionally also accept external report data  157  (or the external report data may be accepted by the analytics engine  109 ). Unlike the data analytics engine  109 , the server  107  receives data from the hub  105  and alerts the alert device  113 . Both the server  107  and analytics engine  109  communicate with the dashboard. The data analytics engine  109  may optionally produce a detailed report  159  and historical data from other sources. 
         [0045]      FIG. 5  shows an example workflow of the server  107 . First,  FIG. 5  shows the bundled sensor reads are received from the hub  105 . In various embodiments, the reads include sensor data from multiple sensors including sound, temperature, and light sensors. The sensor data also, as previously described includes a unique identifier  123  and time stamp information. By analyzing the packet contents, the server  107  can determine whether a particular sensor device  103  has stopped functioning or has limited connectivity, based on the time stamp frequency associated with the unique device identifier  123 . The server  107  may also detect abnormal reads and dropped packets. 
         [0046]    Next, the server  107  may save the new sensor read data S 301  for active and historical purposes. As shown in step S 303 , the server  107  queries historical sensor data—the system, in various embodiments, uses a change in data to predict disruption or damage. As such, the active current values read by the sensors may be less important than the historical context or environment. Historical data interpreted in conjunction with the recently obtained data may indicate the development of a positive or negative trend. 
         [0047]    In step S 305  the interpreted data may be provided to a user dashboard  111 . In various embodiments, the sensor reads may need to be changed from raw values to values a user more easily can understand. The dashboard  111  may be a graphical user interface allowing the user to view sensor data based on sensor unit  103  or hub  105  locations. The sensor device  103  or hub  105  location may be determined and transmitted by the server  107  or other system component by way of associating the unique device number  125  with the particular sensor unit  103  or hub  105  as configured in its location. The dashboard  111  may allow the user to see historical data results across the system implementation. For example, a user may be able to display visually the historic sensor data trends over a period of months. The historic sensor data may be displayed across all installed sensor units  103  or hubs  105  and associated with all hubs within the system installation. Likewise, a dashboard may be provided for those with broader access abilities, for example, allowing a regional healthcare administrator to see the sensor trends across many hospitals. 
         [0048]    Looking to step S 307 , in the example of a data packet containing sound information obtained from a sound sensor  117 , the sound information may be normalized using a normalization formula. In various embodiments, a novel way of quantifying the amount of sound over a reference level is used (“score”). For example, if a sound level (for example a decibel input) is obtained from a room and a threshold sound level value set, a “score” may be determined as, in various embodiments, the difference between the sound level input and a maximum allowable sound level. 
         [0049]    This normalized value may be used to compare sound data packet information to produce a “score” or meaningful differentiation between disruptive and non-disruptive sound levels. Where the reference decibel is the maximum allowable sound level, the score represents the duration and intensity of sound above the maximum allowable decibel level for any user-defined period of time in any user-defined location or locations. The practical effect of using this method is an improvement over the current method of average decibel level using sample readings. Unlike existing methods, this “score” allows the comparison of a value over the desired sound level over various periods of time or various locations. By comparing with historical values, trends are revealed allowing users to make change necessary to affect those trends. The trends may be used by the data analytics engine  109 . 
         [0050]    In step S 309 , configuration and sensor unit  103  or hub  105  locations may be obtained. For example, a sound may be disruptive in one context (a hospital room around 1:00 AM) than another (a public area around 3:00 PM). Such nuances may be saved in the server  107  data storage of configuration settings  151  associated with the particular system installation or sensor device (unit or hub)  103 / 105 . The configuration settings  151  may also have adjusted threshold levels depending on location of the sensor unit (public place versus private room, outdoor versus indoor). 
         [0051]    Depending on the configuration settings, the server  107  may query one or more nearby sensor units  103  or hubs  105  for sensor data in step S 311 . By querying for nearby sensor data, if the threshold for alert for one sensor is higher (for example, louder sounds may be allowed in a public area versus room; colder temperatures may be allowed in an outdoor space versus indoors), the disruption may still impact a sensor unit in a different location (for example, noise in a public area may disrupt a bedroom). Whether or not nearby sensor units  103  or hubs  105  are queried for sensor data, the server  107  may still query configuration settings S 313 , S 315 . The server  107  may then determine whether or not to alert S 317 . If the server  107  determines no alert should be sent, the system may do nothing S 321 . If the server  107  determines the readings warrant an alert, an alert will be provided to associated systems S 319 . Associated systems may include cellular phones which may receive an alert in the form of a text message, a dashboard as described above which may display alerts, or any other appropriate means. For example, the sensor device  103  or hub  105  may have audio or visual means for transmitting an alert such as a beep or blinking lights. 
         [0052]    An example alert device may be an independent fob  177  worn by personnel. An example of the components of the fob  177  is provided in  FIG. 9 . The fob  177  may have an encasement. The encasement may be made of any suitable material, for example, plastic. The fob  177  may have a spring clip for attachment to a shirt, a hole for a lanyard attachment, or any other suitable attachment means. The fob  177  may include a microcontroller  179  which may include a radio frequency (RF) network module and processor. The fob may further include devices such as, but not limited to, a wireless circuit and/or antenna  181 , a power supply/regulation  183 , connector(s)  185 , LEDs  187 , and a motor  189 . In various embodiments, the fob  177  may vibrate or provide other ways to alert a wearer of an active or potential disruption. The vibration may be enabled by the motor  189 . In various embodiments, the microcontroller  179  having the RF module and/or wireless circuit/antenna  181  may be an end-point network device used with a Zigbee or Bluetooth personal wireless network. In various embodiments, details as to battery life, connectivity, or other messages may be communicated by the fob  177  using LEDs  187 . The power supply and regulation  183  as well as connectors  185  may allow the fob  177  to connect to power (such as a battery) and/or charging sources using any suitable means such as, but not limited to, a USB connection (standard, micro, mini, etc.), rail charging system, and/or induction system (for example, a wireless induction system). In a non-limiting example of a rail charging system, the fob  177  or fobs may be clipped onto a specially-built charging rail, wherein the sides of the rail are charged such that when a device is clipped onto the rail(s) the clip and body contact both sides of the rail and make an electrical connection. This non-limiting example may produce more efficient charging than a wireless induction system. 
         [0053]    In an example implementation, when the fob  177  is first turned on, it may flash one or more LEDs  187  once every few seconds to indicate that it is on but not connected to a network. The fob  177  may then connect to a network using the wireless circuit/antenna  181  and/or microcontroller  179 , which may include an RF Module. The fob  179  may use an increasing frequency technique to pair with the closest sensor unit  103  or hub  105  quickly without using excess power. Once the fob  177  connects to a network (which may, in various embodiments, be the network provided by a hub  105  and/or sensor unit  103 ), the fob  177  may flash the LEDs  187  to indicate its connection and then stop flashing. In various embodiments, the connected hub  105  and/or sensor unit  103  may likewise be programmed to indicate a connection to a fob  177  by flashing. 
         [0054]    The fob  177  may be used, for example, to alert a wearer when the noise levels in a room have reached a threshold. The fob  177  may, in various embodiments, regularly query its connected sensor unit  103  or hub  105  to determine whether this threshold has been reached. The fob  177  may then respond, for example, by vibrating (using a motor  189 ) and/or flashing (using LEDs  187 ) to notify a wearer of the exceeded threshold level. This may allow a wearer to abate the exceeded threshold problem before it becomes a disruption. While in this example, a sensor unit  103  or hub  105  detecting a particular area may pair directly with one or more fobs  177  in that particular area, it should be understood that information obtained throughout a network of units  103  and/or hubs  105  can be transmitted to a connected fob  177  (no matter which sensor unit  103  or hub  105  the fob  177  is connected to). As such, the fob  177  could transmit any number of different types of alerts to a wearer. For example, the fob  177  could notify the wearer of: an alert in any particular room, to check the dashboard  111  or other alert unit  113 , to indicate a particular alarm code for a particular type of harm (a hospital alarm code or patient code, for example), a particular exceeded threshold (exceeded temperature or exceeded detected movement, for example), or any other suitable message. The fob  177  for example could alert the wearer with a variety of means such as vibration and blinking using its components. 
         [0055]    Returning to step S 323  of  FIG. 5 , after determining whether to alert, the server  107  may send associated data to the data analytics engine  109  (though it should be understood that at any point in the logic flow of  FIG. 5 , data could be sent from the server  107  to data analytics engine  109 ). At a general level shown in  FIG. 3 , when the analytics engine  109  receives the data in step S 213 , the analytics engine  109  may, in step S 215  analyze the data and possibly create a predictive report S 217 . This process is shown in more detail in  FIG. 6 . The data received from the server  107  is used to create a new data signature S 401 . A data signature, in various embodiments, can be a bundled results history of sensor information. The data signature may reflect a trend over a period of time. In step S 403  the analytics engine  109  may obtain relevant historical signature data. This data may come from a variety of sources, including a combination of historical data reads and other data sources such as surveys. For example, the surveys may reflect whether a person felt disrupted during a period of time. The processing/analytics engine  109  will analyze the time period and correlate sensor unit data during that time to create new historical data signature information. In step S 405 , the data signature is compared with historical data signature information. By executing this comparison in data, the engine may use a disruption anticipation algorithm to anticipate whether or not a disruption may occur. For example, if the sound variance is similar to a disruption sound variance, though the actual decibel levels or raw read data may differ, the trend may lead to report of a disruption. Likewise, if peak noises are made at certain time intervals correlative with a non-disruptive data signature, though the decibel levels may be high, the engine  109  may predict no disruption. This disruption potential metric is provided to a user in step S 407 . In various embodiments, this may take the form of providing a report to an administrator or other interested party. 
         [0056]    In step S 409 , actual disruption data may be obtained. In various embodiments, this may be a survey or other questionnaire given to a patient or resident that reflects their actual disruption impression. This information may be entered back into an accessible database, and the disruption signature data may be updated. In turn, the disruption signature prediction undergoes machine learning, increasing in accuracy through use of the system. 
         [0057]      FIG. 7  presents an example of the system implemented in a hospital. Here, two areas are shown  501 / 503 . These areas may be on multiple floors. Each area may be provided with its own hub  105 , H 1  or H 2 . Each hub may therefore create its own wireless area network (WAN 1 , WAN 2 ) throughout which the sensor units  103  and hubs  105  communicate. The example may use Zigbee-compliant devices. Therefore, communication between the sensors  103  and hub  105  is shown to use means that may or may not be compliant with a shortest-path algorithm. In this non-limiting example, each sensor unit  103  does not communicate directly with the hub  105 . The example shows various noises  509 , and occupied rooms  511 . The example may also show a first and second nurse&#39;s station  505 ,  507 , which may include a dashboard  111  and/or alert unit  113 . The hospital may have a variety of patient rooms housing sensor units  103 . 
         [0058]    The dashboard may reflect which rooms are occupied  511  and their location. The dashboard may reflect current sensor read levels from the sensor unit  103  within the occupied or unoccupied room. In zone  1   501 , a noise  509  causing a change in sound reading occurs in the lower left-hand room housing sensor unit S 3 . The sound reading may be sent to the hub  105 , which in turn may send information to the server  107 . The server  107  may determine whether the unit is occupied (it is not), whether the adjacent room experienced a change in reading, whether the adjacent room is occupied (it is not). It may calculate whether the adjacent unit showed a disruption (it did not). It may consider the main room housing the nurses&#39; station  505  and sensor hub  105  (having sensors) in calculating the disruption. It may consider the configuration for loudness levels in the main room as compared to patient rooms (for example, higher loudness levels may be allowed in a main area rather than a patient room). It may look to whether the patient room across the main room experienced a change in readings, whether it is occupied, and whether the change constitutes a disruption. If all of these do not show a disruption, the system may or may not decide to provide an alert to the dashboard  111  or alert device  113 . The system may alert personnel to determine whether a noise in an unoccupied room should be alerted to for potential equipment damage. 
         [0059]    The noise may be considered in the production of a sleep report, which provides personnel with the number of sleep opportunities for patients overnight. Sleep opportunities may be determined, in various embodiments, as the number of times a certain sleep interval occurs per night. For example, if a sleep interval is 1.5 hours, the number of sleep opportunities in a nine hour period would be six. The system may generate a sleep opportunity report and, given its occurrence in an unoccupied room and its effect on surrounding rooms, the sound  509  may not be considered disruptive. 
         [0060]    In contrast, in the second area  503 , a change in sound reading levels  509  occurs in the main room near the nurse&#39;s station. By checking the adjacent rooms, the system can see the change is also detected in the room housing sensor unit S 8 . Because this unit is occupied, the system may alert the dashboard  111  and alert device  113  to alert personnel to abate the problem before it becomes disruptive. The alert device may continue to alert if the problem is not abated. In various embodiments, it may wait a certain time to re-alert. This may depend on the alert detected by the sensor—for example, if the temperature exceeds a certain threshold, it may take longer than for the violated threshold triggering the alert to be abated. Therefore, the re-alert settings may be configured accordingly. 
         [0061]    This occurrence may be considered by the analytics engine  109  to determine whether, in the context of the readings as a whole (the data signature), a disruptive stay by the patient may be reported. After a patient leaves the hospital after their stay, they may complete a report. The report information may be entered into the system, and the prediction provided by the analytics engine  109  may be updated to reflect the actual reports. 
         [0062]    The system and methods described herein may be implemented in or by software. To this end, the methods may be implemented in a general purpose software package or a specific purpose software package. Multiple system devices can be monitored and combined with the software application, and can be further isolated for review and evaluation. Additional details may also be used and added to the software through one or more fields or entry points, permitting filtering or further characterization of the data obtained by the system. It is understood that the foregoing is provided for purposes of example only, and variations thereon are acceptable. For example, the application may be an application program interface (API), which provides the user the ability to customize the particular software system for the purposes or uses of the particular facility. 
         [0063]    According to one or more examples of embodiments a variety of capabilities is provided when one or more sensors or microsystems are linked in the described system, with particularly advantageous operation being provided when a plurality of sensors or sensing microsystems are used. A variety of capabilities is also provided when the network of sensors or sensing microsystems is connected to the Internet. In addition to the foregoing, when provided access to the Internet, the system can obtain and use, or submit or otherwise provide real time or aggregated sensed data to an outside entity, such as but not limited to a utility company or other service provider, or other data destinations. In addition to data compilation, the external communication provides alarms, alerts, or other information to a user on designated device based on sensed events. Data can also be received from such an outside entity. Likewise, Internet connectivity allows for the system to receive new analysis or control algorithms or other software/firmware upgrades, as well as data usable by the system, such as current and forecasted weather information for inclusion in processing by the predictive algorithm. Internet or remote connectivity also permits the system to receive user commands from the user&#39;s computer, network-device, smartphone, or other stationary or portable data communication device. While specific examples are provided, a variety of other useful functions are enabled by network connectivity. 
         [0064]    As described herein, in one or more examples of embodiments, the system, method, and devices described, or method embodied by software, may be implemented by a computer system or in combination with a computer system. The computer system may be or include a processor. The computers for use with the methods and various components described herein may be programmable computers which may be special purpose computers or general purpose computers that execute the system according to the relevant instructions. The computer system can be an embedded system, a personal computer, notebook computer, tablet computer, server computer, mainframe, networked computer, handheld computer, personal digital assistant, workstation, and the like. Other computer system configurations may also be acceptable including, smartphones, cell phones, mobile devices, multiprocessor systems, microprocessor-based or programmable electronics, network PC&#39;s, minicomputers, and the like. Preferably, the computing system chosen includes a processor suitable in size to efficiently operate one or more of the various systems or functions. 
         [0065]    The system or portions thereof may also be linked to a distributed computing environment, where tasks are performed by remote processing devices that are linked through a communications network. To this end, the system may be configured or linked to multiple computers in a network, including, but not limited to a local area network, a wide area network, a wireless network, and the Internet. Therefore information and data may be transferred within the network or system by wireless means, by hardwire connection or combinations thereof. Wireless communication may be by Wi-Fi, Bluetooth, RF, and other now known or future developed means. The sensors or microsystems are each configured to communicate using a wireless communication protocol such as Wi-Fi, ZigBee, or Z-Wave. The wireless communications among the multiple sensing sensors or microsystems can be achieved in a networked fashion using a wireless router, on an ad hoc or peer-to-peer basis, various combinations thereof, or any other method that can be used to achieve wireless communication. 
         [0066]    The computer can also include a display, provision for data input and output, etc. Furthermore, the computer or computers may be operatively or functionally connected to one or more mass storage devices, such as, but not limited to a database. The database and/or server(s) may be local or cloud based. The memory storage can be volatile or non-volatile and can include removable storage media. The system may also include computer-readable media which may include any computer readable media or medium that may be used to carry or store desired program code that may be accessed by a computer. The invention can also be embodied as computer readable code on a computer readable medium. To this end, the computer readable medium may be any data storage device that can store data which can be thereafter read by a computer system. Examples of computer readable medium include read-only memory, RAM, CD-ROM, CD-R, CD-RW, magnetic tapes, and other optical data storage devices, memory cards, USB flash drives, solid-state drives, etc. The computer readable medium can also be distributed over a network coupled computer system so that the computer readable code is stored and executed in a distributed fashion. 
         [0067]    These devices include a graphical user interface (GUI) or a communication means by which commands may be entered and content, notification, and alerts may be displayed or communicated. For example, the computer may include a user interface that allows navigation of objects. The computer may implement or include an application that enables a user to display and interact with text, images, videos, data, and other information and content. 
         [0068]    Aspects of the system and method described herein can be implemented on software running on a computer system. The system or method herein, therefore, may be operated by computer-executable instructions, such as but not limited to program modules, executable on a computer. Examples of program modules include, but are not limited to, routines, programs, objects, components, data structures and the like which perform particular tasks or implement particular instructions. The software system may also be operable for supporting the transfer of information within a network. 
         [0069]    The systems and devices described may include physical hardware and firmware configurations, along with hardware, firmware, and software programming that is capable of carrying out the currently described methods. A person skilled in the art would understand that the physical hardware and firmware configurations and the hardware, firmware, and software programming that embody the physical and functional features described herein can be implemented without undue experimentation using publicly available hardware and firmware components and known programming tools and development platforms. 
         [0070]    It is further contemplated that the system may be further arranged with objects or devices capable of performing tasks including, but not limited to: operating the one or more physical environment systems according to a schedule and sensed occupancies; providing a user interface for easy modification; providing feedback on the user display regarding occupant usage and usage patterns; learning about the preferences, habits, and occupancy patterns of the building occupants by virtue of sensor detection patterns; adapting to the learned preferences, habits, and occupancy patterns by static and/or dynamic modification; modeling or otherwise characterizing one or more capabilities of the system and its components; and optimizing the system based on the determined characteristics or data of the physical environment and/or the learned occupant preferences, habits, and occupancy patterns. 
         [0071]    As utilized herein, the terms “approximately,” “about,” “substantially”, and similar terms are intended to have a broad meaning in harmony with the common and accepted usage by those of ordinary skill in the art to which the subject matter of this disclosure pertains. It should be understood by those of skill in the art who review this disclosure that these terms are intended to allow a description of certain features described and claimed without restricting the scope of these features to the precise numerical ranges provided. Accordingly, these terms should be interpreted as indicating that insubstantial or inconsequential modifications or alterations of the subject matter described and claimed are considered to be within the scope of the invention as recited in the appended claims. 
         [0072]    It should be noted that references to relative positions (e.g., “top” and “bottom”) in this description are merely used to identify various elements as are oriented in the figures. It should be recognized that the orientation of particular components may vary greatly depending on the application in which they are used. 
         [0073]    For the purpose of this disclosure, the term “coupled” means the joining of two members directly or indirectly to one another. Such joining may be stationary in nature or moveable in nature. Such joining may be achieved with the two members or the two members and any additional intermediate members being integrally formed as a single unitary body with one another or with the two members or the two members and any additional intermediate members being attached to one another. Such joining may be permanent in nature or may be removable or releasable in nature. 
         [0074]    It is also important to note that the construction and arrangement of the system, methods, and devices as shown in the various examples of embodiments is illustrative only. Although only a few embodiments have been described in detail in this disclosure, those skilled in the art who review this disclosure will readily appreciate that many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.) without materially departing from the novel teachings and advantages of the subject matter recited. For example, elements shown as integrally formed may be constructed of multiple parts or elements show as multiple parts may be integrally formed, the operation of the interfaces may be reversed or otherwise varied, the length or width of the structures and/or members or connector or other elements of the system may be varied, the nature or number of adjustment positions provided between the elements may be varied (e.g. by variations in the number of engagement slots or size of the engagement slots or type of engagement). The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the various examples of embodiments without departing from the spirit or scope of the present inventions. 
         [0075]    While this invention has been described in conjunction with the examples of embodiments outlined above, various alternatives, modifications, variations, improvements and/or substantial equivalents, whether known or that are or may be presently foreseen, may become apparent to those having at least ordinary skill in the art. Accordingly, the examples of embodiments of the invention, as set forth above, are intended to be illustrative, not limiting. Various changes may be made without departing from the spirit or scope of the invention. Therefore, the invention is intended to embrace all known or earlier developed alternatives, modifications, variations, improvements and/or substantial equivalents. 
         [0076]    The technical effects and technical problems in the specification are exemplary and are not limiting. It should be noted that the embodiments described in the specification may have other technical effects and can solve other technical problems.

Technology Classification (CPC): 6