Correlating Sensor Data Obtained from a Wearable Sensor Device with Data Obtained from a Smart Phone

• US 20140343380A1
• Filed: 05/15/2014
• Published: 11/20/2014
• Est. Priority Date: 05/15/2013
• Status: Abandoned Application

• Current Assignee: Abraham Carter, David Scott
• Original Assignee: Abraham Carter, David Scott
• Inventors: Carter, Abraham, Scott, David
• Application Number:
  US14/279,140
• Publication Number:
  US 20140343380A1
• Time in Patent Office: Days
• US Class Current: 600/309
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/6802 Sensor mounted on worn items
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/63 for local operation

Abstract

Sensor data obtained from a wearable sensor device can be correlated with sensor data obtained from a smart phone or other mobile device. The correlation of the data from the two sources can enable the determination of why a person performs some action during sleep. In a particular example, motion data obtained from a wearable sensor device can be correlated with audio or visual data obtained by a sensor on a smart phone. In this way, it can be determined whether a person moved in response to a sound or light perceived during sleep. Additionally, the correlation of the data from the two sources can also provide additional information about how a user performs an activity such as exercise.

Claims

1. A method, performed by a mobile device, for correlating sensor data received from a wearable sensor device with sensor data received from one or more sensors within the mobile device, the method comprising:

receiving first sensor data from one or more sensors of the mobile device, the first sensor data representing an environmental occurrence while a user is sleeping;

storing the first sensor data with an indication of a first time at which the first sensor data was generated;

receiving second sensor data from one or more wearable sensor devices that are worn by the user while the user is sleeping, the second sensor data being generated by one or more sensors in the one or more wearable sensor devices, the second sensor data representing a movement the user made while sleeping;

storing the second sensor data with an indication of a second time at which the second sensor data was generated;

analyzing the first and second sensor data including determining that the duration of time between the first time and the second time is below a specified threshold; and

creating a correlation between the environment occurrence and the movement.

2. The method of claim 1, wherein the environmental occurrence is one of a sound or light.

3. The method of claim 1, wherein the correlation includes a strength.

4. The method of claim 3, wherein the strength of the correlation is based on one or more of:

an intensity of the environmental occurrence;

or

the duration of time between the first time and the second time.

5. The method of claim 1, wherein the one or more sensors of the mobile device comprise a microphone.

6. The method of claim 1, wherein the one or more sensors in the one or more wearable sensor devices comprise one or more accelerometers.

7. A method, performed by a mobile device, for correlating sensor data received from a wearable sensor device with sensor data received from one or more sensors within the mobile device, the method comprising:

receiving first sensor data from one or more sensors of the mobile device;

storing the first sensor data with an indication of a first time at which the first sensor data was generated;

receiving second sensor data from one or more wearable sensor devices that are worn by the user while the user is sleeping, the second sensor data being generated by one or more sensors in the one or more wearable sensor devices, the second sensor data representing a change in a physiological parameter of the user while the user is sleeping;

storing the second sensor data with an indication of a second time at which the second sensor data was generated;

analyzing the first and second sensor data including determining that the duration of time between the first time and the second time is below a specified threshold; and

creating a correlation between the environment occurrence and the change in the physiological parameter.

8. The method of claim 7, wherein the physiological parameter is the user'"'"'s heart rate.

9. The method of claim 7, wherein the physiological parameter is the user'"'"'s hemoglobin saturation.

10. The method of claim 7, wherein the correlation includes a strength.

11. The method of claim 10, wherein the strength of the correlation is based on one or more of:

an intensity of the environmental occurrence;

or

the duration of time between the first time and the second time.

12. The method of claim 7, wherein the one or more sensors of the mobile device comprise a microphone.

13. The method of claim 7, wherein the one or more sensors in the one or more wearable sensor devices comprise one or more accelerometers.

14. One or more computer storage media storing computer executable instructions which when executed perform a method for correlating sensor data received from a wearable sensor device with sensor data received from one or more sensors within a mobile device, the method comprising:

receiving first sensor data from one or more sensors of the mobile device;

storing the first sensor data with an indication of a first time at which the first sensor data was generated;

receiving second sensor data from one or more wearable sensor devices that are worn by the user, the second sensor data being generated by one or more sensors in the one or more wearable sensor devices;

storing the second sensor data with an indication of a second time at which the second sensor data was generated;

analyzing the first and second sensor data including determining that the duration of time between the first time and the second time is below a specified threshold; and

creating a correlation between first sensor data and the second sensor data based on the duration of time being below the specified threshold.

15. The computer storage media of claim 14, wherein the first sensor data represents an audible occurrence, and the second sensor data represents motion.

16. The computer storage media of claim 14, wherein the first sensor data represents a visible occurrence, and the second sensor data represents motion.

17. The computer storage media of claim 14, wherein the second sensor data is generated while the user is sleeping.

18. The computer storage media of claim 14, wherein the second sensor data is generated while the user is exercising.

19. The computer storage media of claim 14, wherein the second sensor data represents motion, and the correlation indicates that the motion likely occurred as a result of an occurrence represented by the first sensor data.

20. The computer storage media of claim 14, wherein the method further comprises:

displaying, on the mobile device, an indication of the correlation.