Patent Publication Number: US-2019192011-A1

Title: Wearable health monitoring data service

Description:
CLAIM OF PRIORITY 
     This application claims the benefit of priority to U.S. Provisional Application Ser. No. 62/581,929, filed on Nov. 6, 2017, which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     Embodiments described herein generally relate to wearable device configuration, data management, and data analysis, in some embodiments, more specifically to a wearable health monitoring data service. 
     BACKGROUND 
     Wearable devices may collect data about a user and their activities and surroundings. A wearable device may be configured in a specific manner to maximize efficiency in collecting data that may be used to evaluate performance of a wearer. The data collected by the wearable may be stored and analyzed to provide the wearer or another individual with information regarding the performance of the wearer. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document. 
         FIG. 1  illustrates an example of a wearable health monitor device and a system for a wearable health monitoring data service, according to an embodiment. 
         FIGS. 2A, 2B, and 2C  illustrate an example of a press and hold user interface for a wearable health monitoring data service, according to an embodiment. 
         FIG. 3  illustrates an example of a process for data management for a wearable health monitoring data service, according to an embodiment. 
         FIG. 4  illustrates an example of a process for battery optimization for a wearable health monitoring data service, according to an embodiment. 
         FIG. 5  is a block diagram illustrating an example of a machine upon which one or more embodiments may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     As the population ages and technology improves, there may be opportunities that seniors to lead more independent lives. As people age, they may develop or be more susceptible to health issues (e.g., disease, illness, etc. injury (e.g., due to falls, decreased activity levels, etc.). Close monitoring of the activity and vital signs of a senior may allow the senior or their caregiver to identify risks. The senior or their caregiver may then take appropriate steps to promote changes to daily activities that may reduce the risk of developing or worsening illness and injury. Traditionally, continuous monitoring of a senior may be facilitated by residency in a care facility which may have physical and emotional consequences for the senior. The senior may feel confined and the continued monitoring may be viewed as undignified. 
     The present subject matter provides a wearable health monitor device and corresponding data service that may be wont by a senior that may monitor the activity and vital signs of the senior. Data may be collected regarding the senior without the need for confinement in a specialized facility or conventional vital sign collection by a caregiver. Thus, the senior may be able to continue living at home or in a less confining facility such as a private unit in an assisted living facility. The data for the senior may be collected and analyzed and compared against statistical models to identify if the activity and vital signs of the senior are meeting the needs of a current care plan and/or whether the data indicates the senior is at an increased risk of health degradation or injury. 
     Sensor data may be collected from the wearable health monitor device for the purpose of improving an individual&#39;s health and the healthcare services they&#39;re offered. As an example, consider a senior individual over the age of 65 years old. Older users may have mobility challenges, mental challenges, or may not be familiar with current technology. Thus, user interfaces may be designed to provide streamlined interaction and easy to understand output. Thus, the present solution may provide value to the senior by providing an easy to use user interface and readily consumable output. In addition, an easy to use interface for activating an emergency response protocol may be provided via the wearable allowing rapid access to a personal emergency response service. 
     The present subject matter may be implemented as a software architecture platform in the wearable health monitor device which may allow for flexibility in developing services and applications to run on the wearable health monitor device. The platform may include a data service that may capture and transmit sensor data to a cloud computing platform (e.g., an internet connected cluster of computing devices, etc.). The platform may include a set of battery optimization algorithms allowing for increased device efficiency and increased run time between charging. 
       FIG. 1  illustrates an example of a wearable health monitor device  100  and a system  105  for a wearable health monitoring data service, according to an embodiment. The wearable health monitor device  100  may be a smartwatch, wristband, smart clothing, or other device including sensors capable of capturing user activity data and vital sign data (e.g., gyroscope, accelerometer, magnetometer, infrared sensor, camera, microphone, gas sensor, photodetector, etc.). 
     The system  105  may be implemented in the wearable health monitor device  100 . The system  100  may be a wearable architecture such as a wearable health monitoring platform that may include a user interface layer  110 , an application layer  115 , a service layer  120 , and a wearable operating system  125 . The system  105  may include a set of machine instructions stored in machine readable memory of the wearable health monitor device  100  that, when executed by processing circuitry of the wearable health monitor device  100 , perform a variety of operations including the capture and transmission of sensor data. 
     The system  105  may be configured to allow for flexibility of the type of data captured and the manner of data transmission. In an example, data capture may include resident sensors on the wearable health monitor device and may include other devices providing sensor readings or data packets that the wearable health monitor device  100  may be able to capture. The other devices including sensors may be connected to the wearable health monitor device  100  using a variety of connectivity techniques such as, for example, Bluetooth, NFC, wireless, cellular, etc. Data transmission may utilize a cloud environment as an end point or any other connected data storage and analysis environment. 
     The system  105  may include a variety of layers. The individual layers may provide a separation of processing operations that may allow for asynchronous development of components within each layer. Such separation may be provided using interface points between components or services of each layer. Each component or service may include a set of instructions that may be a part of (or may work in conjunction with) processing circuitry that may cause hardware and software components in the wearable health monitor device  100  to perform a variety of operations. 
     The wearable operating system  125  may provide basic operating instructions that provide basic input, basic output, post, and other basic operability functions for the wearable health monitor device  100 . The wearable device  100  may include communication hardware such as, for example, a cellular network transceiver. The wearable operating system  125  may facilitate communication between components of the system  105  and the communication hardware. 
     The service layer  120  may work in conjunction with the wearable operating system  125  to manage data capture and data transmission functions of the wearable heath monitor device  100 . For example, the service layer  120  may manage an array of sensors included in the wearable health monitor device  100  to collect sensor readings at various intervals and transmit the sensor data from the wearable health monitor device  100  to the cloud-based service. 
     In an example, the service layer  120  may include a capture data service that first captures sensor data from the wearable health monitor device. This would include data such as battery level, heart rate readings, step count reading and GPS location readings. This capture data service would capture the information and manage the local storage of the data. 
     In an example, the service layer  120  may include a transmission data service that would track available connections (e.g., cellular, etc.) and transmit data when the connection allows. 
     The service layer  120  may include a scheduling data service that coordinates the execution of the capture data service and once completed, initiates the data transmission service. 
     The application layer  115  may provide interfaces between the wearable operating system  125 , the service layer  120 , the user interface layer  110 , the cloud-based service, and other devices. In an example, the application layer  115  may include a wireless communication application (e.g., Bluetooth, etc.) that connects to a wireless enabled device (e.g., a weight scale, a blood pressure monitor, etc.) to collect sensor readings from sensors included in the devices. The wireless communication application may utilize the capture data service of the service layer  120  to store the sensor readings in a local storage medium. 
     The user interface layer may provide instructions for generating graphical interfaces with which the user may interact with the wearable health monitor device  100 . In an example, the user interface layer  110  may include a wearable user interface that allows the user to initiate the heart rate sensor and to place an emergency call using a Press and Hold User Interface. 
       FIGS. 2A, 2B, and 2C  illustrate an example of a press and hold user interface  200  for a wearable health monitoring data service, according to an embodiment. A user may touch the screen (or otherwise activate an emergency call feature) of a wireless health monitor device. The press and hold user interface  200  may be displayed (e.g., as shown in  FIG. 2A ). As the user holds the screen, the press and hold user interface  200  may provide a graphical user interface element showing the progress of the request (e.g., as shown in  FIG. 2B ). The delay in activating the call may prevent false activations for users that may have mobility issues or may be prone to accidental screen touches (e.g., via bumping, accidental swipes, etc.). The delay and corresponding visual elements may be adjusted for individual users and for groups of users depending on conditions and needs of the user and group. For example, a delay may be shortened for a user with a heart condition while the delay may be increased for a user with hand tremors, and so forth. Once the delay has been reached, the visual indicator may reach a final stage (e.g., as shown in  FIG. 2C ) and the call may be placed. 
       FIG. 3  illustrates an example of a process  300  for data management for a wearable health monitoring data service, according to an embodiment. 
     The present subject matter may include a wearable data service. The wearable data service may include, a set of functions and algorithms that manage data capture of a wearable health monitor device&#39;s sensors, a set of functions and algorithms that analyze the quality of the sensor reading, a light weight local relational database that provides persistent storage of the sensor readings and allows for ingress and egress of data as well as flexibility to add or modify sensor readings, a set of functions and algorithms that manage the reading and packaging of sensor readings from the local relational database, a set of functions and algorithms that manage the transmission of data packets from the wearable to non-local cloud storage via cellular, Wi-Fi etc. 
     The wearable data service may include a set of functions and algorithms that manage the schedule for data management functions such as, for example, initiating sensor readings, analyzing sensor readings, interfacing with the local relational database, reading and packaging of local data, transmitting local data packets to non-local cloud storage, etc. The wearable data service may optimize a wearable health monitor device to perform operations such as, for example, collection of heart rate reading(s) from the wearable health monitor device, storage of the heart rate reading(s) locally on a storage medium of the wearable health monitor device, and transmission of the heart rate reading(s) from the wearable health monitor device to non-local cloud storage via a network (e.g., wireless network, near-field communication, wired network, short-wave radio, etc.). 
     The wearable data service may utilize a schedule function to periodically check the local time and invoke a heart rate check every 15 minutes. The service then invokes a function that listens for the latest heart rate reading (e.g., at operation  305 ). Upon receiving the latest heart rate reading, the service checks against the current local time. If the interval is greater than 15 minutes (e.g., as determined at decision  310 ), it invokes a heart rate collection function (e.g., at operation  315 ). The heart rate collection invokes a function that attempts to collect a heart rate for a period of 10 seconds. If successful, it records the reading and proceeds. If unsuccessful, it retries the same function but adjusts the period to 15 seconds (e.g., at operation  305 ). This retry loop can be adjusted dynamically. The retry loop also has an unsuccessful threshold level that when reached, will record an unsuccessful attempt. 
     Upon either a successful attempt or unsuccessful attempt, the service then invokes a data quality check that evaluates the accuracy of the reading (e.g., at operation  320 ). For any successful reading, it uses a custom algorithm to label the reading as “accurate” or “not accurate” (e.g., at operation  325 ). For any unsuccessful reading, it labels the reading as “not accurate”. After labeling each reading, the service compiles a SQL statement to insert the record into a local persistent database (e.g., at operation  330 ). The service invokes the statement. The service writes to a local audit log to determine if the statement was successful and the process  300  ends (e.g., at end  335 ). 
       FIG. 4  illustrates an example of a process  400  for battery optimization for a wearable health monitoring data service, according to an embodiment. A variety of battery optimization techniques may be used to maximize the run time of the wearable health monitor device between recharge. These techniques may increase the sensor data collection cycles that may be performed between charges and may reduce the frequency with which a senior or caregiver must recharge the wearable health monitor. This may increase the ease of use and independence of seniors using the devices, especially those with arthritis or other ailments affecting dexterity, by reducing potentially unwanted visits into their personal space (e.g., private apartment, etc. 
     The battery optimization algorithms are based on the operating characteristics of the wearable health monitor device. Power draw of individual sensors (e.g., heart rate sensor, GPS, etc.) of the wearable health monitor device may be approximated. Power draw of individual functions (e.g., data transmission request, etc.) may be approximated. Characteristics of individual functions (e.g., accuracy of sensor readings, connection strength, etc. may be assessed. The power draw approximations and characteristics may be used to model and test different combinations of sensor readings and function calls to assess performance relating to approximate power draw, reliability, and accuracy of a modeled combination. Based on characteristics of individual sensor/functions and combination(s), new data collection processes may be developed based on the modeling and testing to collect data while incurring lowest possible power draw. 
     In an example, connection strength may be used in optimizing battery usage in data transmission. This may result in shorter data transmission times resulting in lower battery usage. The connection strength may be assessed (e.g., at operation  405 ). Connection strength may be based on signal strength, available bandwidth, or other characteristics of the connection indicative of reduced data transmission times. If the connection is determined not to be strong (e.g., at decision  410 ), the wearable health monitor device may wait to reattempt data collection (e.g., at operation  315 ). A threshold may be employed to determine if a connection is strong. For example, if currently detected bandwidth is below fifty percent of an expected available bandwidth value the connect may not be determined as strong while current available bandwidth above fifty percent of the expected bandwidth value may be determined to be a strong connection. If the connection is strong (e.g., as determined at decision  410 ), it may be determined in a recent record for the sensor collection item in a local database (e.g., at decision  420 ). If there is not a recent record in the local database, the sensor reading may be collected and transmitted (e.g., at operation  425 ). If there is a recent record in the local database, the sensor reading may be package and transmitted (e.g., at operation  430 ). 
       FIG. 5  illustrates a block diagram of an example machine  500  upon which any one or more of the techniques (e.g., methodologies) discussed herein may perform. In alternative embodiments, the machine  500  may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine  500  may operate in the capacity of a server machine, a client machine, or both in server-client network environments. In an example, the machine  500  may act as a peer machine in peer-to-peer (P2P) (or other distributed) network environment. The machine  500  may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein, such as cloud computing, software as a service (SaaS), other computer cluster configurations. 
     Examples, as described herein, may include, or may operate by, logic or a number of components, or mechanisms. Circuit sets are a collection of circuits implemented in tangible entities that include hardware (e.g., simple circuits, gates, logic, etc.). Circuit set membership may be flexible over time and underlying hardware variability. Circuit sets include members that may, alone or in combination, perform specified operations when operating. In an example, hardware of the circuit set may be immutably designed to carry out a specific operation (e.g., hardwired). In an example, the hardware of the circuit set may include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.) including a computer readable medium physically modified (e.g., magnetically, electrically, moveable placement of invariant massed particles, etc.) to encode instructions of the specific operation. In connecting the physical components, the underlying electrical properties of a hardware constituent are changed, for example, from an insulator to a conductor or vice versa. The instructions enable embedded hardware (e.g., the execution units or a loading mechanism) to create members of the circuit set in hardware via the variable connections to can out portions of the specific operation when in operation. Accordingly, the computer readable medium is communicatively coupled to the other components of the circuit set member when the device is operating. In an example, any of the physical components may be used in more than one member of more than one circuit set. For example, under operation, execution units may be used in a first circuit of a first circuit set at one point in time and reused by a second circuit in the first circuit set, or by a third circuit in a second circuit set at a different time. 
     Machine (e.g., computer system)  500  may include a hardware processor  502  (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory  504  and a static memory  506 , some or all of which may communicate with each other via an interlink (e.g., bus)  508 . The machine  500  may further include a display unit  510 , an alphanumeric input device  512  (e.g., a keyboard), and a user interface (UI) navigation device  514  (e.g., a mouse). In an example, the display unit  510 , input device  512  and UI navigation device  514  may be a touch screen display. The machine  500  may additionally include a storage device (e.g., drive unit)  516 , a signal generation device  518  (e.g., a speaker), a network interface device  520 , and one or more sensors  521 , such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor. The machine  500  may include an output controller  528 , such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), cellular, etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.). 
     The storage device  516  may include a machine readable medium  522  on which is stored one or more sets of data structures or instructions  524  (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions  524  may also reside, completely or at least partially, within the main memory  504 , within static memory  506 , or within the hardware processor  502  during execution thereof by the machine  500 . In an example, one or any combination of the hardware processor  502 , the main memory  504 , the static memory  506 , or the storage device  516  may constitute machine readable media. 
     While the machine readable medium  522  is illustrated as a single medium, the term “machine readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) configured to store the one or more instructions  524 . 
     The term “machine readable medium” may include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine  500  and that cause the machine  500  to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Non-limiting machine readable medium examples may include solid-state memories, and optical and magnetic media. In an example, a massed machine readable medium comprises a machine readable medium with a plurality of particles having invariant (e.g., rest) mass. Accordingly, massed machine-readable media are not transitory propagating signals. Specific examples of massed machine readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. 
     The instructions  524  may further be transmitted or received over a communications network  526  using a transmission medium via the network interface device  520  utilizing any one of a number of transfer protocols (e.g., frame relay, Internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as Wi-Fi®, IEEE 802.16 family of standards known as WiMax®), IEEE 802.15.4 family of standards, peer-to-peer (P2P) networks, among others. In an example, the network interface device  520  may include one or more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more antennas to connect to the communications network  526 . In an example, the network interface device  520  may include a plurality of antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine  500 , and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.