SYSTEM AND METHOD FOR ACTIVITY DETERMINATION

• US 20160089080A1
• Filed: 06/05/2015
• Published: 03/31/2016
• Est. Priority Date: 09/30/2014
• Status: Abandoned Application

• Current Assignee: Mophie Incorporated (Evercel, Inc.)
• Original Assignee: Mophie Incorporated (Evercel, Inc.)
• Inventors: Li, Fang, Chen, Zhihao, Cao, Cheng
• Application Number:
  US14/732,438
• Publication Number:
  US 20160089080A1
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 5/6803 Head-worn items, e.g. helmets, masks, headphones or goggles
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: G01C 22/006 Pedometers
• CPC Class Codes: G01P 15/00 Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration

Abstract

Features are disclosed relating to determining an activity in which a user is (or has been) engaged. One such activity is the taking of steps (e.g., walking or running). Some embodiments described herein are directed to accurate detection and counting of steps made by a user wearing a device with step-detection functionality. The accurate step counting can be facilitated by detecting signatures of certain activities, and determining whether to count steps based on an analysis of acceleration data over various intervals and moving windows of time.

Claims

1. A device comprising:

one or more sensors configured to detect acceleration;

one or more physical computer processors configured to at least;

obtain acceleration data from the one or more sensors for a time interval;

determine that the acceleration data is not indicative of a non-step-related activity;

average the acceleration data for the time interval to obtain a mean acceleration vector;

project the mean acceleration vector on a gravity acceleration vector to obtain a projection;

identify a peak using a derivative based at least partly on the projection; and

determine that a user of the device has taken a step based at least partly on the identified peak.

2. The device of claim 1, wherein the one or more computer processors are configured to determine that the acceleration data is not indicative of a non-step-related activity by extracting one or more features from the acceleration data and analyzing the features to determine whether they correspond to features of data associated with a non-step-related activity.

3. The device of claim 1, wherein the non-step-related activity comprises driving, eating, brushing teeth, or sleeping.

4. The device of claim 1, wherein the one or more computer processors are configured to determine that the acceleration data is not indicative of a non-step-related activity by analyzing the acceleration data with respect to data associated with mechanical vibrations.

5. The device of claim 1, wherein the one or more computer processors are further configured to determine whether a number of peaks identified in a window of time exceeds a threshold.

6. The device of claim 1, wherein the one or more sensors comprises a 3-axis accelerometer.

7. The device of claim 1, wherein the one or more processors are further configured to obtain data from a plurality of sensors.

8. The device of claim 7, wherein the plurality of sensors comprises an accelerometer and a gyroscope.

9. The device of claim 1, wherein the one or more processors are configured to at least:

identify a plurality of steps during a time window;

determine that the user is performing a stepping activity when the plurality of steps during the time window are above a threshold number; and

determine that the user is not performing a stepping activity when the plurality of steps during the time window are not above the threshold number.

10. Non-transitory computer-readable storage storing executable instructions that cause one or more computer processors to perform a process comprising:

obtaining acceleration data for a first time interval;

determining that the acceleration data is indicative of an active state of a user;

averaging the acceleration data for the first time interval to obtain a mean acceleration vector;

projecting the mean acceleration vector on a gravity acceleration vector to obtain a projection;

identifying a peak using a derivative based at least partly on the projection; and

determining that the user has taken a step based at least partly on the identified peak.

11. The non-transitory computer-readable medium of claim 10, wherein determining that the acceleration data is indicative of an active state of a user comprises using a classification model trained to detect a signature of user activity in acceleration data.

12. The non-transitory computer-readable medium of claim 10, wherein the process further comprises:

obtaining acceleration data for a second time interval;

determining that the acceleration data is indicative of an inactive state of a user;

changing a rate at which acceleration data is sampled for a third time interval.

13. The non-transitory computer-readable medium of claim 12, wherein the process further comprises:

obtaining acceleration data for the third time interval;

determining that a movement magnitude represented by the acceleration data fails to exceed an idle state threshold; and

determining that the user is sleeping based at least partly on the determination that the movement magnitude fails to exceed the idle state threshold.

14. The non-transitory computer-readable medium of claim 12, wherein the process further comprises:

obtaining acceleration data for the third time interval;

determining that a movement magnitude represented by the acceleration data exceeds an idle state threshold; and

determining that the user is not sleeping based at least partly on the determination that the movement magnitude exceeds the idle state threshold.

15. The non-transitory computer-readable medium of claim 12, wherein the process further comprises:

obtaining acceleration data for the third time interval;

determining that a movement magnitude represented by the acceleration data fails to exceed a sleeping state threshold; and

determining that the acceleration for the third time interval is indicative of non-use.

16. A system comprising:

one or more sensors configured to detect acceleration;

one or more computer processors configured to at least;

obtain acceleration data from the one or more sensors for a first time interval;

determine that the acceleration data for the first time interval is indicative of an active state of a user;

obtain acceleration data from the one or more sensors for a second time interval;

determine that the acceleration data for the second time interval is indicative of an inactive state of a user; and

change a rate at which acceleration data is sampled for a third time interval.

17. The system of claim 16, wherein the one or more computer processors are configured to determine that the acceleration data is indicative of an active state of a user comprises using a classification model trained to detect a signature of user activity in acceleration data.

18. The system of claim 16, wherein the one or more computer processors are configured to at least:

average the acceleration data for the first time interval to obtain a mean acceleration vector;

project the mean acceleration vector on a gravity acceleration vector to obtain a projection;

identify a peak using a derivative based at least partly on the projection; and

determine that the user has taken a step based at least partly on the identified peak.

19. The system of claim 16, wherein the one or more computer processors are further configured to at least:

obtain acceleration data for the third time interval;

determine that a movement magnitude represented by the acceleration data fails to exceed an idle state threshold; and

determine that the user is sleeping based at least partly on the determination that the movement magnitude fails to exceed the idle state threshold.

20. The system of claim 16, wherein the one or more computer processors are further configured to at least:

obtain acceleration data for the third time interval;

determine that a movement magnitude represented by the acceleration data exceeds an idle state threshold; and

determine that the user is not sleeping based at least partly on the determination that the movement magnitude exceeds the idle state threshold.

21. The system of claim 16, wherein the one or more computer processors are further configured to at least:

obtain acceleration data for the third time interval;

determine that a movement magnitude represented by the acceleration data fails to exceed a sleeping state threshold; and

determine that the acceleration for the third time interval is indicative of non-use.

22. The system of claim 16, wherein the one or more processors are configured to at least:

identify a plurality of steps during a time window;

determine that the user is performing a stepping activity when the plurality of steps during the time window are above a threshold number; and

determine that the user is not performing a stepping activity when the plurality of steps during the time window are not above the threshold number.