SYSTEM AND METHOD FOR ACTIVITY CLASSIFICATION

• US 20160051167A1
• Filed: 08/21/2014
• Published: 02/25/2016
• Est. Priority Date: 10/10/2012
• Status: Abandoned Application

• Current Assignee: Invensense Incorporated (TDK Corporation)
• Original Assignee: Invensense Incorporated (TDK Corporation)
• Inventors: Lin, Shang Hung, Saha, Sankalita, Shekar, Hemabh, Chang, Chih-Chieh Geoff
• Application Number:
  US14/464,999
• Publication Number:
  US 20160051167A1
• 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 5/7264 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: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A system and method for efficiently and accurately classifying user activity. In a non-limiting example, accelerometer signals and/or gyroscope sensor signals may be analyzed to classify user activity, for example, that of a user of a handheld and/or wearable device. Information from additional sources and sensors (e.g., other inertial sensors, non-inertial sensors, passive sensors, etc.) may also be utilized.

Claims

1. A system for activity classification, the system comprising:

at least one module operable to, at least;

receive a sensor signal;

form a plurality of filtered sensor signals by, at least in part, filtering the received sensor signal with a plurality of respective band-pass filters; and

identify an activity class based, at least in part, on the plurality of filtered sensor signals.

2. The system of claim 1, wherein the received sensor signal is expressed in a world coordinate system.

3. The system of claim 2, wherein the received sensor signal comprises a z-axis component of an accelerometer sensor signal expressed in the world coordinate system.

4. The system of claim 1, wherein the at least one module is operable to form at least four filtered sensor signals by, filtering the received sensor signal with at least four respective band-pass filters.

5. The system of claim 1, wherein the at least one module is operable to adapt the characteristics of the plurality of respective band-pass filters.

6. The system of claim 1, wherein the at least one module is operable to identify an activity class by, at least in part, determining a respective indication of energy for each of the plurality of filtered sensor signals.

7. The system of claim 6, wherein the at least one module is operable to identify an activity class by, at least in part, determining a respective running scaled sum for each of the respective indications of energy.

8. The system of claim 6, wherein the at least one module is operable to identify an activity class based, at least in part, on an immediately prior identified activity.

9. The system of claim 6, wherein the at least one module is operable to identify an activity class by, at least in part, utilizing a decision tree in which the respective indications of energy for each of the plurality of filtered sensor signals are analyzed to traverse the decision tree.

10. The system of claim 9, wherein the at least one module is operable to adapt decision criteria in the decision tree based, at least in part, on monitored user behavior.

11. The system of claim 1, wherein the at least one module is operable to identify an activity class by, at least in part, identifying the activity class at a variable rate.

12. The system of claim 1, wherein the at least one module is operable to:

determine an indication of the variability of an accelerometer signal and/or another signal derived therefrom; and

identify the activity further based, at least in part, on the determined indication of variability.

13. The system of claim 1, wherein the at least one module is operable to condition the received sensor signal to restore signal shape.

14. The system of claim 1, wherein the at least one module is operable to determine a confidence level associated with the identified activity class.

15. The system of claim 1, wherein the at least one module is operable to provide an interface by which a user of the system can define at least a portion of a set of activity classes from which the identified activity class is selected.

16. The system of claim 1, wherein the at least one module is operable to:

receive location information; and

identify the activity further based, at least in part, on the received location information.

17. The system of claim 1, wherein the at least one module is operable to:

receive pedometer information; and

identify the activity further based, at least in part, on the received pedometer information.

18. The system of claim 1, wherein the at least one module is operable to:

receive cadence information; and

identify the activity further based, at least in part, on the received cadence information.

19. The system of claim 1, wherein the at least one module is operable to:

receive proximity sensor information; and

identify the activity further based, at least in part, on the received proximity sensor information.

20. The system of claim 1, wherein the at least one module is operable to:

receive magnetometer information; and

identify the activity further based, at least in part, on the received magnetometer information.

21. A system for activity classification, the system comprising:

at least one module operable to, at least;

receive an inertial sensor signal from an inertial sensor;

receive a non-inertial sensor signal from a non-inertial sensor;

form a plurality of filtered inertial sensor signals by, at least in part, filtering the received inertial sensor signal with a plurality of respective band-pass filters; and

identify an activity class based, at least in part, on the plurality of filtered inertial sensor signals and the non-inertial sensor signal.

22. The system of claim 21, wherein the non-inertial sensor signal comprises a proximity sensor signal.

23. The system of claim 21, wherein the non-inertial sensor signal comprises a pressure sensor signal.

24. The system of claim 21, wherein the non-inertial sensor signal comprises a microphone signal.

25. The system of claim 21, wherein the non-inertial sensor signal comprises a magnetometer signal.

26. A system for activity classification, the system comprising:

at least one module operable to, at least;

receive a sensor signal;

receive an orientation signal;

form a plurality of filtered sensor signals by, at least in part, filtering the received sensor signal with a plurality of respective band-pass filters;

identify an activity class based, at least in part, on the plurality of filtered sensor signals;

determine a tilt indication based, at least in part on the received orientation signal and on the identified activity class; and

output one or more signals indicative of the identified activity class and the determined tilt indication.

27. The system of claim 26, wherein the received orientation signal comprises a quaternion.

28. The system of claim 26, wherein the at least one module is operable to determine a tilt indication by, at least in part:

determining a tilt change relative to a reference tilt; and

comparing the determined tilt change to a tilt threshold.