AUTOMATIC DETECTION OF A WEARABLE ELECTRONIC DEVICE NOT BEING WORN USING A MOTION SENSOR

• US 20160022175A1
• Filed: 10/07/2015
• Published: 01/28/2016
• Est. Priority Date: 09/23/2014
• Status: Abandoned Application

• Current Assignee: Fitbit, Inc. (Alphabet Inc.)
• Original Assignee: Fitbit, Inc. (Alphabet Inc.)
• Inventors: ARNOLD, Jacob Antony, VENKATRAMAN, Subramaniam
• Application Number:
  US14/877,922
• Publication Number:
  US 20160022175A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0209 adapted for power saving
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 2562/04 Arrangements of multiple sensors of the same type
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1121 Determining geometric values, e.g. centre of rotation or angular range of movement
• CPC Class Codes: A61B 5/1122 of movement trajectories
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7278 : Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/743 : Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots

Abstract

Aspects of automatically determining a period of time when a wearable electronic device is not being worn are discussed herein. For example, in one aspect, an embodiment may automatically determine a period of time when the wearable electronic device is not being worn based on a comparison of the motion data and a not-worn profile that specifies a pattern of motion data that is indicative of when the wearable electronic device is not being worn by a user. The wearable electronic device also stores in the non-transitory machine readable storage medium, data associating the period of time with a not-worn state.

Claims

1. A wearable electronic device comprising:

a set of one or more motion sensors to generate motion data;

a set of one or more processors; and

a non-transitory machine readable storage medium coupled to the set of motion sensors and the set of one or more processors, the non-transitory machine readable storage medium having stored therein instructions, which when executed by the set of processors, cause the wearable electronic device to;

automatically determine a period of time when the wearable electronic device is not being worn based on a comparison of the motion data and a not-worn profile that specifies a pattern of motion data that is indicative of when the wearable electronic device is not being worn by a user, and

store, in the non-transitory machine readable storage medium, data associating the period of time with a not-worn state.

2. The wearable electronic device of claim 1, wherein the motion data includes motion data samples, and the instructions, when executed by the set of processors, cause the wearable electronic device to automatically determine that the wearable electronic device is not worn for the period of time based on causing the wearable electronic device to:

determine that a number of the motion data samples failing to meet a motion measurement threshold is below a threshold number.

3. The wearable electronic device of claim 1, wherein the non-transitory machine readable storage medium also stores additional data associating additional periods of time with the not-worn state, the period of time and the additional periods of time together representing consecutive periods of time, and wherein the instructions, when executed, also cause the wearable electronic device to:

derive a span of time covering the consecutive periods of time based on the data and the additional data associating the consecutive periods of time with the not-worn state; and

store, in the non-transitory machine readable storage medium, data associating the span of time with the not-worn state.

4. The wearable electronic device of claim 3, wherein the instructions that, when executed by the set of processors, cause the derivation of the span of time cause the wearable electronic device to:

detect that the consecutive periods of time include at least a threshold number of consecutive periods of time.

5. The wearable electronic device of claim 1, wherein the instructions, when executed by the set of processors, also cause the wearable electronic device to:

automatically cause, based on the not-worn state for the period of time, one or more of a reduction in power consumption of the wearable electronic device, a discontinuation of storage of sensor data from the set of one or more sensors of the wearable electronic device, a communication of data to another electronic device, and a receipt of a firmware update from another electronic device.

6. The wearable electronic device of claim 1, wherein the instructions, when executed by the set of processors, also cause the wearable electronic device to automatically determine another period of time when the wearable electronic device is not being worn based on a subsequent comparison of the motion data and another not-worn profile that specifies a different pattern of motion data that is indicative of when the wearable electronic device is not being worn by the user.

7. The wearable electronic device of claim 1, wherein the pattern of motion data characterizes an orientation of the wearable electronic device.

8. The wearable electronic device of claim 7, wherein the pattern of motion data further characterizes an acceptable range of motion for the orientation.

9. The wearable electronic device of claim 7, wherein the pattern of motion data characterizes the orientation based on a threshold force of acceleration along one or more axes consistent with a force of gravity being applied along the one or more axes.

10. The wearable electronic device of claim 9, wherein the one or more axes represents an axis running across a display of the wearable electronic device.

11. The wearable electronic device of claim 9, wherein the one or more axes represents an axis running through a display of the wearable electronic device.

12. The wearable electronic device of claim 7, wherein the pattern of motion data characterizes the orientation based on a threshold force of acceleration along one or more axes consistent with a force of gravity being applied along the one or more axes.

13. The wearable electronic device of claim 12, wherein the threshold force of acceleration accounts for a determinable degree of tilt by the wearable electronic device.

14. The wearable electronic device of claim 1, wherein the instructions, when executed by the set of processors, also cause the wearable electronic device to:

transmit to another electronic device the data representing the association of the not-worn state and the period of time.

15. The wearable electronic device of claim 1, wherein the instructions, when executed by the set of processors, also cause the wearable electronic device to:

automatically determine a subsequent period of time when the wearable electronic device is being worn based on a subsequent comparison of the motion data and the not-worn profile that specifies the pattern of motion data that is indicative of when the wearable electronic device is not being worn by the user, and

store, in the non-transitory machine readable storage medium, data associating the subsequent period of time with a worn state.

16. A method executed by a set of one or more processors of a wearable electronic device, the method comprising:

obtaining motion data generated by a set of one or more motion sensors of the wearable electronic device;

automatically determining a period of time when the wearable electronic device is not being worn based on a comparison of the motion data and a not-worn profile that specifies a pattern of motion data that is indicative of when the wearable electronic device is not being worn by a user; and

storing, in a non-transitory machine readable storage medium of the wearable electronic device, data associating the period of time with a not-worn state.

17. The method of claim 16, wherein the motion data includes motion data samples, and the determining that the wearable electronic device is not being worn for the period of time includes:

determining that a number of the motion data samples failing to meet a motion measurement threshold is below a threshold number.

18. The method of claim 16, further comprising:

storing, in the non-transitory machine readable storage medium, additional data associating additional periods of time with the not-worn state, the period of time and the additional periods of time together representing consecutive periods of time;

deriving a span of time covering the consecutive periods of time based on the data and the additional data associating the consecutive periods of time with the not-worn state; and

storing, in the non-transitory machine readable storage medium, data associating the span of time with the not-worn state.

19. The method of claim 18, wherein the deriving comprises:

detecting that the consecutive periods of time includes at least a threshold number of consecutive periods of time.

20. The method of claim 16, further comprising:

automatically causing, based on the not-worn state for the period of time, one or more of a reduction in power consumption of the wearable electronic device, a discontinuation of storage of sensor data from the set of one or more sensors of the wearable electronic device, a communication of data to another electronic device, and a receipt of a firmware update from another electronic device.

21. The method of claim 16, further comprising:

automatically determining another period of time when the wearable electronic device is not being worn based on a subsequent comparison of the motion data and another not-worn profile that specifies a different pattern of motion data that is indicative of when the wearable electronic device is not being worn by the user.

22. The method of claim 16, wherein the pattern of motion data characterizes an orientation of the wearable electronic device.

23. The method of claim 22, wherein the pattern of motion data further characterizes an acceptable range of motion for the orientation.

24. The method of claim 22, wherein the pattern of motion data characterizes the orientation based on a threshold force of acceleration along one or more axes consistent with a force of gravity being applied along the one or more axes.

25. The method of claim 24, wherein the one or more axes represents an axis running across a display of the wearable electronic device.

26. The method of claim 24, wherein the one or more axes represents an axis running through a display of the wearable electronic device.

27. The method of claim 22, wherein the pattern of motion data characterizes the orientation based on a threshold force of acceleration along one or more axes consistent with a force of gravity being applied along the one or more axes.

28. The method of claim 27, wherein the threshold force of acceleration accounts for a determinable degree of tilt by the wearable electronic device.

29. The method of claim 16, further comprising:

transmitting to another electronic device the data representing the association of the not-worn state and the period of time.

30. The method of claim 16, further comprising:

determining that a subsequent period of time when the wearable electronic device is being worn based on a subsequent comparison of the motion data and the not-worn profile that specifies the pattern of motion data that is indicative of when the wearable electronic device is not being worn by the user; and

storing, in the non-transitory machine readable storage medium, data associating the subsequent period of time with a worn state.