Device and method for classifying the activity and/or counting steps of a user

• US 10,441,197 B2
• Filed: 05/30/2014
• Issued: 10/15/2019
• 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
• Primary Examiner(s): Hindenburg, Max F
• Application Number:
  US15/315,090
• Publication Number:
  US 20170188897A1
• Time in Patent Office: 1,964 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.

5. 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.

6. 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.

7. The method as claimed in claim 6, comprising applying a smoothing to the accelerometer data prior to identifying the most active axis.

8. The method as claimed in claim 7, comprising identifying the most active axis by determining the axis having the largest mean absolute value of the smoothed signal.

9. The method as claimed in claim 6, comprising classifying the activity based on a maximum absolute signal amplitude of the accelerometer data for the most active axis.

10. The method as claimed in claim 6, comprising identifying the most active axis and classifying the activity of the user based on accelerometer data measured over a predetermined processing window.