MOVEMENT MEASURE GENERATION IN A WEARABLE ELECTRONIC DEVICE

• US 20160007934A1
• Filed: 09/18/2015
• Published: 01/14/2016
• 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 9,808,185 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 generating movement measures that quantify motion data samples generated by a wearable electronic device are discussed herein. For example, in one aspect, an embodiment may obtain motion data samples generated by the set of motion sensors in a wearable electronic device. The wearable electronic device may then generate a first movement measure based on a quantification of the first set of motion data samples. The wearable electronic device may also generate a second movement measure based on a quantification of the second set of the motion data samples. The wearable electronic device may then cause the non-transitory machine readable storage medium to store the first movement measure and the second movement measure as time series data.

Claims

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

a set of one or more motion sensors to generate motion data samples that represent motion of the wearable electronic device, the motion data samples include a first set of one or more motion data samples generated during a first time interval and a second set of one or more motion data samples generated during a second time interval;

a set of one or more processors coupled to the set of motion sensors; 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 set of one or more processors to;

obtain the motion data samples generated by the set of motion sensors,

generate a first movement measure based on a quantification of the first set of motion data samples,

generate a second movement measure based on a quantification of the second set of the motion data samples, and

cause the non-transitory machine readable storage medium to store the first movement measure and the second movement measure as time series data.

2. The wearable electronic device of claim 1, wherein the instructions, which when executed by the set of one or more processors, cause the set of one or more processors to:

assign a sleep state to the user of the wearable electronic device for a block of time that covers the first time interval based on an analysis that includes the first movement measure and the second movement measure.

3. The wearable electronic device of claim 2, wherein the instructions, when executed by the set of one or more processors, cause the set of one or more processors to perform the analysis that includes the first movement measure and the second movement measure by causing the set of one or more processors to:

derive a value for a feature corresponding to a period of time, the value being calculated from movement measures within in a window of time that includes the first movement measure and the second movement; and

select the sleep state to be assigned to the block of time based on an analysis of values of the feature for a plurality of periods of time covered by the block of time, the plurality of periods including the period of time.

4. The wearable electronic device of claim 3, wherein the instructions, when executed by the set of one or more processors, also cause the set of one or more processors to perform the analysis of values of the feature for the plurality of periods of time covered by the block of time by causing the set of one or more processors to:

determine levels of activity of the user for the plurality of periods of time, wherein the determination of a level of activity of an individual period of time is based on a comparison of a value of the feature corresponding with the individual period of time with a threshold value, and the levels are used in the selection of the sleep state.

5. The wearable electronic device of claim 1, wherein the quantification of the first set of motion data samples includes a transformation of the first set of motion data samples into a single numerical value.

6. The wearable electronic device of claim 1, wherein the quantification of the motion data comprises a statistical measure of the motion data along an axis of motion.

7. The wearable electronic device of claim 6, wherein the statistical measure is one or more of a quantile, an inter-quantile range, a measure of dispersion, and a measure of entropy.

8. The wearable electronic device of claim 6, wherein the statistical measure is derived from a frequency domain analysis of the motion data.

9. The wearable electronic device of claim 8, wherein the frequency domain analysis quantifies an amount of periodic motion and compares the amount of periodic motion in two frequency bands.

10. The wearable electronic device of claim 1, wherein the quantification of the motion data comprises a function of a maximum of the motion data and a minimum of the motion data along an axis.

11. The wearable electronic device of claim 10, wherein the function of the maximum motion data and the minimum motion data along the axis comprises application of a weighted value.

12. The wearable electronic device of claim 1, wherein the quantification of the motion data comprises a calculation of a standard deviation of the motion data along an axis.

13. The wearable electronic device of claim 1, wherein the quantification of the motion data comprises a calculation of a temporal derivative of the motion data along an axis.

14. The wearable electronic device of claim 1, wherein the quantification of the motion data comprises a combination of an inter-quantile range of the motion data along an axis.

15. A method executed by a wearable electronic device, the method comprising:

obtaining a first set of one or more motion data samples generated by a set of one or more motion sensors, the first set of motion data samples generated during a first time interval;

generating a first movement measure based on a quantification of the first set of motion data samples;

obtaining a second set of one or more motion data samples generated by the set of motion sensors, the second set of motion data samples generated during a second time interval;

generating a second movement measure based on a quantification of the second set of the motion data samples; and

causing a non-transitory machine readable storage medium to store the first movement measure and the second movement measure as time series data.

16. The method of claim 15, further comprising:

assigning a sleep state to a user of the wearable electronic device for a block of time that covers the first time interval based on an analysis that includes the first movement measure and the second movement measure.

17. The method of claim 16, wherein the analysis that includes the first movement measure and the second movement measure includes:

deriving a value for a feature corresponding to a period of time, the value being calculated from movement measures within in a window of time that includes the first movement measure and the second movement; and

selecting the sleep state to be assigned to the block of time based on an analysis of values of the feature for a plurality of periods of time covered by the block of time, the plurality of periods including the period of time.

18. The method of claim 17, wherein the analysis of values of the feature for the plurality of periods of time covered by the block of time includes:

determining levels of activity of the user for the plurality of periods of time, wherein the determination of a level of activity of an individual period of time is based on a comparison of a value of the feature corresponding with the individual period of time with a threshold value, and the levels are used in the selection of the sleep state.

19. The method of claim 15, wherein quantification of the first set of motion data samples includes transforming of the first set of motion data samples into a single numerical value.

20. The method of claim 15, wherein the quantification of the motion data comprises a statistical measure of the motion data along an axis.

21. The method of claim 20, wherein the statistical measure is one or more of a quantile, an inter-quantile range, a measure of dispersion, and a measure of entropy.

22. The method of claim 20, wherein the statistical measure is derived from a frequency domain analysis of the motion data.

23. The method of claim 22, wherein the frequency domain analysis quantifies an amount of periodic motion and compares the amount of periodic motion in two frequency bands.

24. The method of claim 15, wherein the quantification of the motion data comprises a function of a maximum of the motion data and a minimum of the motion data along an axis.

25. A computer readable storage device that includes instructions that, when executed by one or more processors, cause the one or more processors to:

obtain a first movement measure that quantifies a first set of motion data generated by a set of one or more motion sensor of a wearable electronic device worn by a user over a first time interval;

obtain a second movement measure that quantifies a second set of motion data generated by the set of motion sensors of the wearable electronic device worn by the user over a second time interval;

classify a level of activity of the user for the first time interval based on an analysis of the first movement measure and the second movement measure; and

use the level of activity to determine a sleep state for a block of time that covers the first time interval.

26. The computer readable storage device of claim 25, wherein the instructions, when executed, cause the one or more processors to analyze the first movement measure and the second movement measure based on deriving a value for a statistical feature calculated based on a statistical comparison that includes the values of the first movement measure and the second movement measure, and the activity level is selected based on the value of the feature.

27. The computer readable storage device of claim 25, wherein the instructions, when executed, cause the one or more processors to quantify the first set of motion data based on an aggregation of multidimensional acceleration data over the first time interval into a single numerical value.

28. The computer readable storage device of claim 25, wherein the quantification of the first set of motion data includes a transformation of the first set of motion data into a single numerical value.

29. The computer readable storage device of claim 25, wherein a device external to and physically separate from the wearable electronic device houses the one or more processors, and the instructions, when executed, cause the one or more processors to obtain the first movement measure and the second movement measure based on a reception of the first movement measure and the second movement measure from the wearable electronic device through a wireless connection.

30. The computer readable storage device of claim 25, wherein the block of time includes time intervals in addition to the first time interval, and the time intervals are classified using a mix of different levels of activity.