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

• US 20160022203A1
• Filed: 10/07/2015
• Published: 01/28/2016
• 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
• Granted Patent: US 10,092,219 B2
• 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 detecting periods of sleep of a user of a wearable electronic device are discussed herein. For example, in one aspect, an embodiment may 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. The wearable electronic device may then classify 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, where the statuses are indicative of relative degree of movement of the user. The wearable electronic device may also 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.

Claims

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

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.

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.

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.

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.

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.

7. The apparatus of claim 6, wherein the PPG data is utilized to calculate at least one of the user'"'"'s:

heart rate data;

heart rate variability data; and

respiration data.

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

after the derivation of the user being in an asleep state, determine, for the periods of time during which the user is in the asleep state, another set of one or more statistical features of which at least one characterizes a distribution of movement of the user, and

classify each of the periods of time, during which the user is in the asleep state, into one of a plurality of sleep stages of the user based on the another set of statistical features, wherein the sleep stages include a rapid eye movement (REM) stage and a plurality of non-REM stages.

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;

galvanic skin response sensor;

capacitive sensor;

humidity sensor; and

sound sensor.

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

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

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.

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.

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.

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.

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.

17. The method of claim 16, wherein the PPG data is utilized to calculate at least one of the user'"'"'s:

heart rate data;

heart rate variability data; and

respiration data.

18. The method of claim 11, further comprising:

after the derivation of the user being in an asleep state, determining, for the periods of time during which the user is in the asleep state, another set of one or more statistical features of which at least one characterizes a distribution of movement of the user, and

classifying each of the periods of time, during which the user is in the asleep state, into one of a plurality of sleep stages of the user based on the another set of statistical features, wherein the sleep stages include a rapid eye movement (REM) stage and a plurality of non-REM stages.

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.

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

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:

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.

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

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.

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.

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.

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.

27. The computer readable storage device of claim 26, wherein the PPG data is utilized to calculate at least one of the user'"'"'s:

heart rate data;

heart rate variability data; and

respiration data.

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:

after the derivation of the user being in an asleep state, determine, for the periods of time during which the user is in the asleep state, another set of one or more statistical features of which at least one characterizes a distribution of movement of the user, and

classify each of the periods of time, during which the user is in an asleep state, into one of a plurality of sleep stages of the user based on the another set of statistical features, wherein the sleep stages include a rapid eye movement (REM) stage and a plurality of non-REM stages.

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.

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