System and method for adjusting step detection based on motion information

• US 20070005246A1
• Filed: 06/30/2005
• Published: 01/04/2007
• Est. Priority Date: 06/30/2005
• Status: Active Grant

• Current Assignee: Nokia Technologies Oy (Nokia Corporation)
• Original Assignee: Nokia Corporation
• Inventors: Kappi, Jani
• Granted Patent: US 7,797,106 B2
• Time in Patent Office: Days
• US Class Current: 701/214
• CPC Class Codes: G01C 22/006 Pedometers
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing
• CPC Class Codes: G06V 40/25 Recognition of walking or running movements, e.g. gait recognition

Abstract

A system and method for changing the weighting accelerometer filtering based upon motion sensor measurements. Using measurements from motion sensors, one or more features can be extracted. This information is used by an algorithm to assign a most appropriate test feature class to be the class of the state of motion at a particular moment in time. This information is used to provide more accurate positioning information, and can also be used to power down the motion sensor at various times, providing an improvement in battery efficiency.

Claims

1. A method improving position measurement of an electronic device, comprising:

measuring movement of the electronic device;

extracting feature information from the measured movement of the electronic device; and

using the extracted feature information to adjust a filtering of information relating to the movement of the electronic device.

2. The method of claim 1, wherein the using of the extracted feature information comprises:

comparing the feature information to a plurality of test feature classes;

selecting one of the plurality of test feature classes that most closely resembles the feature information; and

using the selected test feature class for adjusting the filtering of information relating to the movement of the electronic device.

3. The method of claim 2, wherein the filtering of information is adjusted based upon an alignment cost for the selected test feature class.

4. The method of claim 2, wherein the plurality of test feature classes consist of stationary, moving, walking, running, unknown and free-fall classes.

5. The method of claim 2, further comprising the steps of:

creating a footfall search window based upon the selected test feature class, the footfall search window relating to the estimated amount of time between footfalls for the selected test feature class;

deactivating a sensor at the initiation of each footfall, the sensor being used to measure the movement of the electronic device;

reactivating the sensor at the initiation of the next footfall search window.

6. The method of claim 5, wherein the sensor comprises an accelerometer.

7. A computer program product for improving position measurement of an electronic device, comprising:

computer code for measuring movement of the electronic device using a sensor;

computer code for extracting feature information from the measured movement of the electronic device; and

computer code for using the extracted feature information to adjust a filtering of information relating to the movement of the electronic device.

8. The computer program product of claim 7, wherein the computer code for using the extracted feature information comprises:

computer code for comparing the feature information to a plurality of test feature classes;

computer code for selecting one of the plurality of test feature classes that most closely resembles the feature information; and

computer code for using the selected test feature class to adjust the filtering of information relating to the movement of the electronic device.

9. The computer program product of claim 8, wherein a log-multivariate probability density function is used to compare the feature information to a plurality of predefined test feature classes.

10. The computer program product of claim 8, wherein the plurality of test feature classes consist of stationary, moving, walking, running, unknown and free-fall classes.

11. The computer program product of claim 8, further comprising:

computer code for creating a footfall search window based upon the selected test feature class, the footfall search window relating to the estimated amount of time between footfalls for the selected test feature class;

computer code for deactivating the sensor at the initiation of each footfall;

computer code for reactivating the sensor at the initiation of the next footfall search window.

12. An electronic device, comprising:

a processor; and

a memory unit including;

computer code for measuring movement of the electronic device using a sensor;

computer code for extracting feature information from the measured movement of the electronic device; and

computer code for using the extracted feature information to adjust a filtering of information relating to the movement of the electronic device.

13. The electronic device of claim 12, wherein the computer code for using the extracted feature information comprises:

computer code for comparing the feature information to a plurality of test feature classes;

computer code for selecting one of the plurality of test feature classes that most closely resembles the feature information; and

computer code for using the selected test feature class to adjust the filtering of information relating to the movement of the electronic device.

14. The electronic device of claim 13, wherein a log-multivariate probability density function is used to compare the feature information to a plurality of predefined test feature classes.

15. The electronic device of claim 13, wherein the memory unit further comprises:

computer code for creating a footfall search window based upon the selected test feature class, the footfall search window relating to the estimated amount of time between footfalls for the selected test feature class;

computer code for deactivating the sensor at the initiation of each footfall;

computer code for reactivating the sensor at the initiation of the next footfall search window.

16. A module, comprising:

a processor;

a sensor operatively connected to the processor; and

a memory unit operatively connected to the processor and including;

computer code for measuring movement of the electronic device using the sensor,

computer code for extracting feature information from the measured movement of the electronic device, and

computer code for using the extracted feature information to adjust a filtering of information relating to the movement of the electronic device.

17. The module of claim 16, wherein the computer code for using the extracted feature information comprises:

computer code for comparing the feature information to a plurality of test feature classes,

computer code for selecting one of the plurality of test feature classes that most closely resembles the feature information, and

computer code for using the selected test feature class to adjust the filtering of information relating to the movement of the electronic device.

18. The module of claim 17, wherein a log-multivariate probability density function is used to compare the feature information to a plurality of predefined test feature classes.

19. The module of claim 17, wherein the memory unit further comprises:

computer code for creating a footfall search window based upon the selected test feature class, the footfall search window relating to the estimated amount of time between footfalls for the selected test feature class;

computer code for deactivating the sensor at the initiation of each footfall;

computer code for reactivating the sensor at the initiation of the next footfall search window.

20. The module of claim 16, wherein the sensor comprises an accelerometer.