Application US20190209022

WEARABLE ELECTRONIC DEVICE AND SYSTEM FOR TRACKING LOCATION AND IDENTIFYING CHANGES IN SALIENT INDICATORS OF PATIENT HEALTH
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US20190209022

1. A wearable electronic device comprising: (4) (3)

a platform configured to be secured to an individual;

a source of electric current; and

a hybrid wireless communication module supported by the platform and receiving electric power from the source of electric current, the hybrid wireless communication system comprising: a first wireless communication sub-module that during operation thereof selectively receives location data in the form of a beacon signal;

a second wireless communication sub-module that during operation thereof selectively receives location data in the form of a global navigation satellite system signal; and

a third wireless communication sub-module that during operation thereof transmits a low-power wide area network signal that provides at least location indicia of the wearable electronic device based on the received location data from at least one of the first and second wireless communication sub-modules.

2. The wearable electronic device of claim 1, further comprising: (9) (4)

a plurality of sensors supported by the platform, at least one of the plurality of sensors that during operation thereof detects at least one of environmental data, activity data and physiological data;

a non-transitory computer readable medium supported by the platform;

a processor supported by the platform and configured to perform a predefined set of operations in response to receiving a corresponding instruction selected from a predefined native instruction set; and

a set of machine codes selected from the native instruction set and operated upon by the processor, the set of machine codes comprising: a machine code that receives the location data from at least one of the first and second wireless communication sub-modules;

a machine code that receives at least one of the environmental data, activity data and physiological data from at least one of the plurality of sensors; and

a machine code that transmits at least one of the received location data, environmental data, activity data and physiological data through the third wireless communication sub-module, wherein each of the machine codes are stored in the non-transitory computer readable medium.

3. The wearable electronic device of claim 2, further comprising a machine code to determine a distance between the wearable electronic device and a source of the beacon signal. (0)

4. The wearable electronic device of claim 2, wherein the machine code that transmits at least one of the received location data, environmental data, activity data and physiological data through the third wireless communication sub-module does so through the hybrid wireless communication module while the at least one of the received location data, environmental data, activity data and physiological data is in substantially unprocessed form. (0)

5. The wearable electronic device of claim 2, wherein the machine code that transmits at least one of the received location data, environmental data, activity data and physiological data through the third wireless communication sub-module does so through the hybrid wireless communication module while the at least one of the received location data, environmental data, activity data and physiological data is in substantially processed form. (0)

6. The wearable electronic device of claim 2, wherein the machine code that transmits at least one of the received location data, environmental data, activity data and physiological data through the third wireless communication sub-module does so through the hybrid wireless communication module while the at least one of the received location data, environmental data, activity data and physiological data is in partially processed form. (0)

7. The wearable electronic device of claim 2, wherein at least one of the sensors that detects physiological data comprises at least one sensor selected from the group consisting of a heart rate sensor, a breathing rate sensor, a temperature sensor, a respiration sensor, a pulse oximetry sensor, a respiratory rate sensor, an oxygen saturation sensor, an electrocardiogram sensor, a cardiac output index sensor, a systematic pressure sensor, a systematic systolic arterial pressure sensor; a systematic diastolic arterial pressure sensor, a systematic mean arterial pressure sensor, a central venous pressure sensor, a pulmonary pressure sensor, a pulmonary systolic arterial pressure sensor, a pulmonary diastolic arterial pressure sensor and a pulmonary mean arterial pressure sensor. (0)

8. The wearable electronic device of claim 2, wherein at least one of the sensors that detects activity data comprises at least one sensor selected from the group consisting of an accelerometer, a gyroscope, a magnetometer, an altimeter, a motion detector and an inertial measurement unit. (0)

9. The wearable electronic device of claim 2, wherein at least one of the sensors that detects environmental data comprises at least one sensor selected from the group consisting of an ambient temperature sensor, an ambient pressure sensor, an ambient humidity sensor, a carbon monoxide sensor, a carbon dioxide sensor, a smoke detector, an ambient light sensor, a motion sensor and a microphone. (0)

10. The wearable electronic device of claim 2, further comprising a nurse call button that is supported by the platform and signally cooperative with the third wireless communication sub-module such that upon activation of the nurse call button, request for assistance is transmitted from the wearable electronic device over the low-power wide area network signal. (1) (0)

13. The wearable electronic device of claim 2, further comprising a machine code to control the transmission of data from at least two of the received location data, environmental data, activity data and physiological data through the third wireless communication sub-module. (0)

14. The wearable electronic device of claim 1, wherein the hybrid wireless communication module does not comprise a cellular wireless communication sub-module. (0)

15. The wearable electronic device of claim 1, wherein the platform is selected from the group consisting of a wrist-worn band, an ankle-worn band, an article of clothing, a bandage, a pair of eyeglasses, a necklace or pendant, a clothing-affixable pin, a clothing-affixable patch and a subcutaneous implant. (0)

16. The wearable electronic device of claim 1, wherein the first wireless communication sub-module selectively receives the location data in the form of a BLE signal. (0)

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53. A method of using a machine learning model to evaluate a health condition of an individual, the method comprising: (2) (4)

acquiring, using a wearable electronic device, location data from at least one of Bluetooth Low Energy location data and global navigation satellite system location data;

acquiring, with a plurality of sensors that are formed as part of the wearable electronic device, at least one of environmental data, activity data and physiological data;

wirelessly transmitting at least one of the location data, environmental data, activity data and physiological data from the wearable electronic device to a wireless low power wide area network receiver using a star topology network; and

executing the machine learning model to provide indicia of the health condition based at least in part on the location data, environmental data, activity data and physiological data, wherein the executing takes place using a non-transitory computer readable medium, a processor and a set of machine codes within a predefined native instruction set such that a predefined set of operations is performed by the processor in response to receiving a corresponding instruction selected from the set of machine codes comprising: a machine code that analyzes at least a portion of at least one of the location data, environmental data, activity data and physiological data once the machine learning model has been trained by at least one machine learning algorithm, and

a machine code that causes the analyzed data to be output to a caregiver.

54. The method of claim 53, wherein the machine learning model is trained by the at least one machine learning algorithm that comprises: (0) (3)

55. The method of claim 53, wherein the machine learning model comprises an unsupervised approach that comprises K-means clustering. (0)

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66. A wearable electronic device for tracking a patient comprising: (0) (5)

a processor;

a power source electrically connected to the processor;

a first wireless communication sub-module comprising a Bluetooth Low Energy chipset communicatively connected to the processor and configured to receive Bluetooth Low Energy location information;

a second wireless communication sub-module comprising a global navigation satellite system chipset communicatively connected to the processor and configured to receive global navigation satellite system location information; and

a third wireless communication sub-module comprising a low-power wide area network chipset communicatively connected to the processor and configured to provide location indicia of the wearable electronic device based on location information from at least one of the first and second wireless communication sub-modules.

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Application US20190209022

WEARABLE ELECTRONIC DEVICE AND SYSTEM FOR TRACKING LOCATION AND IDENTIFYING CHANGES IN SALIENT INDICATORS OF PATIENT HEALTH

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