Application US20160022203

AUTOMATIC DETECTION OF USER'S PERIODS OF SLEEP AND SLEEP STAGE
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US20160022203

1. An apparatus for automatically detecting periods of sleep of a user of a wearable electronic device, the apparatus comprising: (8) (2)

a set of one or more processors; and

a non-transitory machine readable storage medium coupled to the set of one or more processors and having stored therein instructions, which when executed by the set of one or more processors, cause the apparatus to: obtain a set of features for periods of time from motion data obtained from a set of one or more motion sensors in the wearable electronic device or data derived therefrom;

classify each of the periods of time into one of a plurality of statuses of the user based on the set of features determined for the periods of time, wherein the statuses are indicative of relative degrees of movement of the user, and

derive blocks of time, each covering one or more of the periods of time, during which the user is in one of a plurality of states, wherein the states include an awake state and an asleep state.

2. The apparatus of claim 1, wherein the classification is performed using a machine learning classifier. (0)

3. The apparatus of claim 1, wherein the plurality of statuses further include a not-worn status, during which the user is not wearing the wearable electronic device. (0)

4. The apparatus of claim 1, wherein the apparatus is one of the wearable electronic device and a secondary electronic device to be coupled to the wearable electronic device. (0)

5. The apparatus of claim 1, wherein one of the set of features is based on a combination of a quantification of a distribution of motion data along each of three axes for one of the one or more periods of time. (0)

6. The apparatus of claim 1, wherein the classification of each of the periods of time is further based on photoplethysmography (PPG) data from a time window that includes that period of time, and wherein the PPG data is generated by a photoplethysmographic sensor in the wearable electronic device. (1) (0)

8. The apparatus of claim 1, wherein the instructions, when executed by the set of processors, also cause the apparatus further to: (0) (2)

9. The apparatus of claim 1, wherein the classification of each of the periods of time is further based on data generated by a set of additional sensors in the wearable electronic device, wherein the set includes one or more of the following: temperature sensor; ambient light sensor; (0) (1)

10. The apparatus of claim 1, wherein the set of motion sensors include an accelerometer. (0)

11. A method for automatically detecting periods of sleep of a user of a wearable electronic device, the method comprising: (8) (3)

obtaining a set of features for periods of time from motion data obtained from a set of one or more motion sensors in the wearable electronic device or data derived therefrom;

classifying each of the periods of time into one of a plurality of statuses of the user based on the set of features determined for the one or more periods of time, wherein the statuses are indicative of relative degrees of movement of the user; and

deriving blocks of time, each covering one or more of the periods of time, during which the user is in one of a plurality of states, wherein the states include an awake state and an asleep state.

12. The method of claim 11, wherein the classifying is performed using a machine learning classifier. (0)

13. The method of claim 11, wherein the plurality of statuses further include a not-worn status, during which the user is not wearing the wearable electronic device. (0)

14. The method of claim 11, wherein the method is performed by one of the wearable electronic device and a secondary electronic device to be coupled to the wearable electronic device. (0)

15. The method of claim 11, wherein one of the set of feature is based on a combination of a quantification of a distribution of motion data along each of three axes for one of the one or more periods of time. (0)

16. The method of claim 11, wherein the classifying of each of the periods of time is further based on photoplethysmography (PPG) data from a time window that includes that period of time, and wherein the PPG data is generated by a photoplethysmographic sensor in the wearable electronic device. (1) (0)

18. The method of claim 11, further comprising: (0) (2)

19. The method of claim 11, wherein the classifying of each of the periods of time is further based on data generated by a set of additional sensors in the wearable electronic device, wherein the set includes one or more of the following: temperature sensor; ambient light sensor; galvanic skin response sensor; capacitive sensor; humidity sensor; and sound sensor. (0)

20. The method of claim 11, wherein the set of motion sensors include an accelerometer. (0)

21. A computer readable storage device that includes instructions that are for automatically detecting periods of sleep of a user of a wearable electronic device and that, when executed by one or more processors, cause the one or more processors to: (8) (3)

obtain a set of features for periods of time from motion data obtained from a set of one or more motion sensors in the wearable electronic device or data derived therefrom;

derive blocks of time, each covering one or more of the periods of time, during which the user is in one of a plurality of states, wherein the states include an awake state and an asleep state.

22. The computer readable storage device of claim 21, wherein the classification is performed using a machine learning classifier. (0)

23. The computer readable storage device of claim 21, wherein the plurality of statuses further include a not-worn status, during which the user is not wearing the wearable electronic device. (0)

24. The computer readable storage device of claim 21, wherein the computer readable storage device is in one of the wearable electronic device and a secondary electronic device to be coupled to the wearable electronic device. (0)

25. The computer readable storage device of claim 21, wherein one of the set of feature is based on a combination of a quantification of a distribution of motion data along each of three axes for one of the one or more periods of time. (0)

26. The computer readable storage device of claim 21, wherein the classification of each of the periods of time is further based on photoplethysmography (PPG) data from a time window that includes that period of time, and wherein the PPG data is generated by a photoplethysmographic sensor in the wearable electronic device. (1) (0)

28. The computer readable storage device of claim 21, wherein the instructions, when executed by the set of processors, also cause the apparatus further to: (0) (2)

29. The computer readable storage device of claim 21, wherein the classification of each of the periods of time is further based on data generated by a set of additional sensors in the wearable electronic device, wherein the set includes one or more of the following: temperature sensor; ambient light sensor; galvanic skin response sensor; capacitive sensor; humidity sensor; and sound sensor. (0)

30. The computer readable storage device of claim 21, wherein the set of motion sensors include an accelerometer. (0)

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Application US20160022203

AUTOMATIC DETECTION OF USER'S PERIODS OF SLEEP AND SLEEP STAGE

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