Automatic change of power consumption of sensors responsive to user's state transition

• US 9,675,281 B2
• Filed: 10/07/2015
• Issued: 06/13/2017
• Est. Priority Date: 09/23/2014
• Status: Active Grant

• Current Assignee: Fitbit, Inc. (Alphabet Inc.)
• Original Assignee: Fitbit, Inc. (Alphabet Inc.)
• Inventors: Arnold, Jacob Antony, Venkatraman, Subramaniam
• Primary Examiner(s): CERIONI, DANIEL LEE
• Application Number:
  US14/877,920
• Publication Number:
  US 20160022201A1
• Time in Patent Office: 615 Days
• Field of Search: 600595
• 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 changing power consumption of a sensor of a wearable electronic device worn by a user are discussed herein. For example, in one aspect, based on detecting that a state of the user, as tracked by the wearable electronic device, has transitioned into an asleep state, an embodiment may decrease power consumption of at least one sensor from the set of sensors. Additionally, based on detecting that the state of the user, as tracked by the wearable electronic device, has transitioned out of the asleep state, the embodiment may reverse the decrease of power consumption of the at least one sensor.

Claims

1. A wearable electronic device to be worn by a user, the wearable electronic device comprising:

one or more sensors configured to detect physiological data or environmental data of the user, the one or more sensors comprising a motion sensor;

one or more processors coupled to the one or more sensors; and

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

calculate a plurality of movement measures, each of the movement measures based on combining data output from the motion sensor during a corresponding time interval;

in response to detecting that a state of the user has transitioned into an asleep state based on the movement measures, decrease power consumption of at least one sensor from the one or more sensors,

in response to detecting that the state of the user has transitioned into an awake state based on the movement measures, reverse the decrease of the power consumption of the at least one sensor, and

during a time period (i) while the user is in the asleep state, (ii) after the decrease of the power consumption, and (iii) before the reverse of the decrease of the power consumption, periodically temporarily increase power consumption of the at least one sensor to generate additional data for classification of the user'"'"'s sleep into at least one of a plurality of stages.

2. The wearable electronic device of claim 1, wherein the one or more sensors includes a photoplethysmographic sensor to generate photoplethysmography (PPG) data of the user, wherein the photoplethysmographic sensor includes a light source and a photodetector.

3. The wearable electronic device of claim 2, wherein the decrease of the power consumption includes at least one of:

a decrease of a sampling rate of the photoplethysmographic sensor,

a decrease of sensitivity of the photoplethysmographic sensor, and

a decrease of a power level of the light source.

4. The wearable electronic device of claim 2, wherein the instructions, when executed by the one or more processors, also cause the wearable electronic device to:

after the decrease, increase the power consumption of the photoplethysmographic sensor to generate additional PPG data for sleep stage detection.

5. The wearable electronic device of claim 4, wherein the increase of the power consumption of the photoplethysmographic sensor includes at least one of:

an increase of a sampling rate of the photoplethysmographic sensor,

an increase of sensitivity of the photoplethysmographic sensor, and

an increase of a power level of the light source.

6. The wearable electronic device of claim 1, wherein the decrease of the power consumption includes at least one of:

an entry into a low precision state of the motion sensor,

a decrease of sensitivity of the motion sensor, and

a decrease of a sampling rate of the motion sensor.

7. The wearable electronic device of claim 1, wherein the motion sensor is an accelerometer.

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

after the decrease and before the reverse, periodically temporarily increase power consumption of the motion sensor to generate additional motion data for classification of the user'"'"'s sleep into at least one of the stages.

9. The wearable electronic device of claim 8, wherein the increase of the power consumption of the motion sensor includes at least one of:

an entry into a high precision state of the motion sensor,

an increase of sensitivity of the motion sensor, and

an increase of a sampling rate of the motion sensor.

28. The wearable electronic device of claim 1, further comprising:

classifying the user'"'"'s sleep into at least one of the plurality of stages based on the additional data.

29. The wearable electronic device of claim 1, further comprising:

transmitting the additional data to a server, the server being configured to classify the user'"'"'s sleep into at least one of the plurality of stages based on the additional data.

10. A wearable electronic device to be worn by a user, the wearable electronic device comprising:

one or more sensors comprising at least a motion sensor configured to generate motion data representing motion of the user;

one or more processors coupled to the one or more sensors;

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

calculate a plurality of movement measures, each of the movement measures based on combining data output from the motion sensor during a corresponding time interval;

based on the movement measures, determine that the user is sleeping;

responsive to the determination that the user is sleeping, increase power consumption of the motion sensor, wherein the increase of the power consumption provides additional data for classification of the user'"'"'s sleep into at least one of a plurality of stages;

based on the movement measures, determine that the user is awake; and

responsive to the determination that the user is awake, reverse the increase of the power consumption of the motion sensor.

11. The wearable electronic device of claim 10, further comprising a photoplethysmographic sensor configured to generate photoplethysmography (PPG) data of the user, wherein the photoplethysmographic sensor includes a light source and a photodetector, wherein the increase in power consumption increases the PPG data generated by the photoplethysmographic sensor, wherein the instructions, when executed by the one or more processors, also cause the wearable electronic device to calculate at least one of:

one or more heart rates of the user, one or more heart rate variabilities of the user, and one or more respiration rates of the user.

12. The wearable electronic device of claim 10, wherein the increase of the power consumption provides additional data for classification of the user'"'"'s sleep into at least one of the stages, and wherein the additional data is motion data used to generate the movement measures based on a combination of a quantification of a distribution of the motion data along each of three axes during that time interval, and wherein each of the movement measures is a single numeric number.

13. The wearable electronic device of claim 10, further comprising a photoplethysmographic sensor configured to generate photoplethysmography (PPG) data of the user, wherein the photoplethysmographic sensor includes a light source and a photodetector, wherein the increase of the power consumption includes at least one of:

an increase of a sampling rate of the photoplethysmographic sensor,

an increase of sensitivity of the photoplethysmographic sensor, and

an increase of a power level of the light source.

14. The wearable electronic device of claim 10, wherein the increase of the power consumption includes at least one of:

an entry into a high precision state of the motion sensor,

an increase of sensitivity of the motion sensor, and

an increase of a sampling rate of the motion sensor.

15. The wearable electronic device of claim 10, further comprising a photoplethysmographic sensor configured to generate photoplethysmography (PPG) data of the user, wherein the photoplethysmographic sensor includes a light source and a photodetector, wherein the light source is a light-emitting diode (LED).

16. A method of managing power consumption of a wearable electronic device to be worn by a user, wherein the wearable electronic device includes one or more sensors configured to detect physiological data or environmental data of the user, the one or more sensors comprising a motion sensor, the method comprising:

calculating a plurality of movement measures, each of the movement measures based on combining data output from the motion sensor during a corresponding time interval;

in response to detecting that a state of the user has transitioned into an asleep state based on the movement measures, decreasing power consumption of at least one sensor from the one or more sensors;

in response to detecting that the state of the user has transitioned into an awake state based on the movement measures, reversing the decrease of the power consumption of the at least one sensor; and

during a time period (i) while the user is in the asleep state, (ii) after the decrease of the power consumption, and (iii) before the reverse of the decrease of the power consumption, periodically temporarily increasing power consumption of the at least one sensor to generate additional data for classification of the user'"'"'s sleep into at least one of a plurality of stages.

17. The method of claim 16, wherein the one or more sensors includes a photoplethysmographic sensor to generate photoplethysmography (PPG) data of the user, wherein the photoplethysmographic sensor includes a light source and a photodetector.

18. The method of claim 17, wherein the decreasing of the power consumption includes at least one of:

a decrease of a sampling rate of the photoplethysmographic sensor,

a decrease of sensitivity of the photoplethysmographic sensor, and

a decrease of a power level of the light source.

19. The method of claim 17, further comprising:

after the decreasing and before the reversing, periodically temporarily increasing power consumption of the photoplethysmographic sensor to generate additional PPG data for classification of the user'"'"'s sleep into at least one of the stages.

20. The method of claim 19, wherein the increasing the power consumption of the photoplethysmographic sensor includes at least one of:

an increase of a sampling rate of the photoplethysmographic sensor,

an increase of sensitivity of the photoplethysmographic sensor, and

an increase of a power level of the light source.

21. The method of claim 16, wherein the decreasing power consumption includes at least one of:

an entry into a low precision state of the motion sensor,

a decrease of sensitivity of the motion sensor, and

a decrease of a sampling rate of the motion sensor.

22. The method of claim 16, further comprising:

after the decreasing and before the reversing, periodically temporarily increasing power consumption of the motion sensor to generate additional motion data for classification of the user'"'"'s sleep into at least one of the stages.

23. The method of claim 22, wherein the increasing power consumption of the motion sensor includes at least one of:

an entry into a high precision state of the motion sensor,

an increase of sensitivity of the motion sensor, and

an increase of a sampling rate of the motion sensor.

24. A method of managing power consumption of a wearable electronic device to be worn by a user, wherein the wearable electronic device includes one or more sensors comprising at least a motion sensor, configured to generate motion data representing motion of the user, the method comprising:

calculating a plurality of movement measures, each of the movement measures based on combining data output from the motion sensor during a corresponding time interval;

responsive to detecting a state of the user transitioning into an asleep state based on the movement measures, increasing power consumption of the motion sensor, wherein the increase of the power consumption provides additional data for classification of the user'"'"'s sleep into at least one of a plurality of stages; and

responsive to detecting the state of the user transitioning into an awake state based on the movement measures, reversing the increase of the power consumption of the motion sensor.

25. The method of claim 24, wherein the wearable electronic device further comprises a photoplethysmographic sensor configured to generate photoplethysmography (PPG) data of the user, wherein the photoplethysmographic sensor includes a light source and a photodetector, wherein the increasing power consumption provides additional data for classification of the user'"'"'s sleep into at least one of the stages, wherein the additional data is PPG data, and wherein the method comprises calculating based on the additional data at least one of:

one or more heart rates of the user, one or more heart rate variabilities of the user, and one or more respiration rates of the user.

26. The method of claim 24, wherein the increasing power consumption provides additional data for classification of the user'"'"'s sleep into at least one of the stages, wherein the additional data is motion data, and wherein the method comprises using the motion data to generate the movement measures based on a combination of a quantification of a distribution of the motion data along each of three axes during that time interval, and wherein each of the movement measures is a single numeric number.

27. The method of claim 24, wherein the wearable electronic device further comprises a photoplethysmographic sensor configured to generate photoplethysmography (PPG) data of the user, wherein the photoplethysmographic sensor includes a light source and a photodetector, wherein the increasing power consumption includes at least one of:

an increase of a sampling rate of the photoplethysmographic sensor,

an increase of sensitivity of the photoplethysmographic sensor, and

an increase of a power level of the light source.