DEVICE AND METHOD FOR CLASSIFYING THE ACTIVITY AND/OR COUNTING STEPS OF A USER

• US 20170188897A1
• Filed: 05/30/2014
• Published: 07/06/2017
• Est. Priority Date: 05/30/2014
• Status: Active Grant

• Current Assignee: Nitto Denko Corporation
• Original Assignee: Nitto Denko Corporation
• Inventors: THEIN, Khine Cho Cho, KASAMSOOK, Kittipong, AUNG, Aye
• Granted Patent: US 10,441,197 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1118 Determining activity level
• 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 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A63B 24/0003 Analysing the course of a movement or motion sequences during an exercise or trainings sequence, e.g. swing for golf or tennis
• 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

Device and method for classifying the activity and/or counting steps of a user. A method for classifying the activity of a user can comprise measuring accelerometer data for a plurality of axes; identifying a most active one of the plurality of axes based on the accelerometer data; and classifying the activity of the user based on a signal amplitude of the accelerometer data for the most active axis and one or more threshold values.

Claims

1. A device for classifying the activity of a user wearing the device, the device comprising:

an accelerometer for measuring accelerometer data for a plurality of axes; and

a processor configured to;

identify a most active one of the plurality of axes based on the accelerometer data; and

classify the activity of the user into different non-stationary activities based on a signal amplitude of the accelerometer data for the most active axis and respective different threshold values for the different non-stationary activities.

2. The device as claimed in claim 1, wherein the processor is configured to apply a smoothing to the accelerometer data prior to identifying the most active axis.

3. The device as claimed in claim 2, wherein the processor is configured to identify the most active axis by determining the axis having the largest mean absolute amplitude of the smoothed signal.

4. The device as claimed in claim 1, wherein the processor is configured to classify the activity based on a maximum signal amplitude of the accelerometer data for the most active axis.

6. The device as claimed in claim 1, wherein the processor is configured to identify the most active axis and to classify the activity of the user based on accelerometer data measured over a predetermined processing window.

5. (canceled)

7. (canceled)

8. A device for counting the number of steps taken by a user wearing the device, the device comprising:

an accelerometer for measuring accelerometer data for at least one axis; and

a processor configured to;

apply a derivative operator to the accelerometer data in successive processing windows;

count peaks in the derivative of the accelerometer data in each processing window; and

eliminate over counted peaks based on time difference between a first peak in a current window and a last peak in a preceding processing window.

9. The device as claimed in claim 8, wherein the processor is configured to count the peaks based on a first threshold value if the current processing window is a first processing window after transiting from a light activity state to a moderate or a heavy activity state.

10. The device as claimed in claim 8, wherein the processor is further configured to update an adaptive threshold for the next window.

11. The device as claimed in claim 10, wherein the adaptive threshold for the next window is based on the difference in signal amplitude for a last successive peak and valley pair in the current processing window.

13. The device as claimed in claim 8, wherein the processor is configured to eliminate peaks from the peak count in the current window if the time difference between the first peak in the current window and the last peak in the preceding processing window is smaller than a second threshold value.

12. (canceled)

14-18. -18. (canceled)

19. A method for classifying the activity of a user, the method comprising:

measuring accelerometer data for a plurality of axes;

identifying a most active one of the plurality of axes based on the accelerometer data; and

classifying the activity of the user into different non-stationary activities based on a signal amplitude of the accelerometer data for the most active axis and respective different threshold values for the different non-stationary activities.

20. The method as claimed in claim 19, comprising applying a smoothing to the accelerometer data prior to identifying the most active axis.

21. The method as claimed in claim 20, comprising identifying the most active axis by determining the axis having the largest mean absolute value of the smoothed signal.

22. The method as claimed in claim 19, comprising classifying the activity based on a maximum absolute signal amplitude of the accelerometer data for the most active axis.

24. The method as claimed in claim 19, comprising identifying the most active axis and classifying the activity of the user based on accelerometer data measured over a predetermined processing window.

23. (canceled)

25. (canceled)

26. A method for counting the number of steps taken by a user, the method comprising:

measuring accelerometer data for at least one axis;

applying a derivative operator to the accelerometer data in successive processing windows;

counting peaks in the derivative of the accelerometer data in each processing window; and

eliminating over counted peaks based on time difference between a first peak in a current window and a last peak in a preceding processing window.

27. The method as claimed in claim 26, comprising counting the peaks based on a first threshold if the current processing window is a first processing window after transiting from a light activity state to a moderate or a heavy activity state.

28. The method as claimed in claim 26, further comprising updating an adaptive threshold for the next window.

29. The method as claimed in claim 28, wherein the adaptive threshold for the next window is based on the difference in signal amplitude for a last successive peak and valley pair in the current processing window.

31. The method as claimed in claim 26, comprising eliminating peaks from the peak count in the current window if the time difference between the first peak in the current processing window and the last peak in the preceding processing window is smaller than a second threshold value.

30. (canceled)

32-33. -33. (canceled)