SYSTEM AND METHOD FOR USER ACTIVITY RECOGNITION USING ACCELEROMETER

• US 20180031605A1
• Filed: 10/28/2016
• Published: 02/01/2018
• Est. Priority Date: 08/01/2016
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: LI, Xue, LI, Linbo, ZOBENKO, Andrey, SONG, Seongwook
• Granted Patent: US 10,564,177 B2
• Time in Patent Office: Days
• CPC Class Codes: G01P 13/00 Indicating or recording presence, absence, or direction, of movement electric switches H01H; counting moving objects G06M7/00
• CPC Class Codes: G01P 15/18 in two or more dimensions

Abstract

A system to recognize user activity includes a filter to form a gravity measurement from a three-dimensional (3D) accelerometer measurement of a user activity; a body component extractor to form a body measurement of the user activity by removing the gravity measurement from the 3D accelerometer measurement; a rotation matrix calculator to form a rotation matrix that is used to rotate the extracted gravity to align with a predetermined direction, and to rotate the 3D accelerometer measurements and the body measurement to be the input to features determiner module; and a features determiner to determine values of a predetermined number of features of the user activity based on the rotated 3D accelerometer measurement and the rotated body measurement in which the predetermined number of features are invariant under arbitrary rotation of the accelerometer measurement around the predetermined direction.

Claims

1. A user activity recognizer, comprising:

a filter to form a gravity measurement from a three-dimensional (3D) accelerometer measurement of a user activity;

a body component extractor to form a body measurement of the user activity by removing the extracted gravity measurement from the 3D accelerometer measurement; and

a rotation matrix calculator to form a rotation matrix R to rotate and align the extracted gravity measurement with a predetermined direction.

2. The user activity recognizer according to claim 1, further comprising a classifier to classify the accelerometer measurement of the user activity as one of a predetermined number of activities based on values of a predetermined number of features of the user activity, the classifier using machine learning to classify the accelerometer measurement of the user activity.

3. The user activity recognizer according to claim 1, further comprising a features determiner to determine values of a predetermined number of features of the user activity based on the rotated 3D accelerometer measurement and the rotated body measurement based on rotation matrix R, the predetermined number of features being invariant under arbitrary rotation of the accelerometer measurement around the predetermined direction.

4. The user activity recognizer according to claim 3, wherein the 3D accelerometer measurement is segmented and each segment comprises N samples, and

wherein the gravity measurement comprises:

5. The user activity recognizer according to claim 4, wherein the 3D accelerometer measurement is received from an accelerometer platform, and

wherein the predetermined direction is in a negative y-axis direction.

6. The user activity recognizer according to claim 5, wherein the accelerometer platform is part of a smartphone.

7. The user activity recognizer according to claim 5, wherein an average of extracted gravity measurement is rotated to substantially align with the negative y-axis of the accelerator platform using the rotation matrix R, wherein

8. The user activity recognizer according to claim 7, wherein

9. The user activity recognizer according to claim 8, wherein components of the rotated body measurement are determined as

10. The user activity recognizer according to claim 9, further comprising a classifier to classify the accelerometer measurement of the user activity as one of a predetermined number of activities based on the values of the predetermined number of features.

11. A method to recognize user activity, the method comprising:

filtering a three-dimensional (3D) accelerometer measurement of a user activity to form a gravity measurement from;

removing the extracted gravity measurement from the 3D accelerometer measurement to form a body measurement of the user activity by; and

rotating and aligning the extracted gravity measurement with a predetermined direction using a rotation matrix R

12. The method according to claim 11, further comprising classifying using machine learning the accelerometer measurement of the user activity as one of a predetermined number of activities based on values of a predetermined number of features of the user activity.

13. The method according to claim 12, further comprising determining values of a predetermined number of features of the user activity based on the rotated 3D accelerometer measurement and the rotated body measurement based on rotation matrix R, the predetermined number of features being invariant under arbitrary rotation of the accelerometer measurement around the predetermined direction.

14. The method according to claim 13, wherein the 3D accelerometer measurement is segmented and each segment comprises N samples, and

wherein the gravity measurement comprises:

15. The method according to claim 14, wherein the 3D accelerometer measurement is received from an accelerometer platform, and

wherein the predetermined direction is in a negative y-axis direction.

16. The method according to claim 15, wherein the accelerometer platform is part of a smartphone.

17. The method according to claim 15, further comprising rotating an average of extracted gravity measurement to substantially align with the negative y-axis of the accelerator platform using the rotation matrix R, wherein

18. The method according to claim 17, wherein

19. The method according to claim 18, further comprising determining components of the rotated body measurement as

20. The method to claim 19, further comprising classifying the accelerometer measurement of the user activity as one of a predetermined number of activities based on the values of the predetermined number of features.