Hierarchical Context Detection Method to Determine Location of a Mobile Device on a Person's Body

• US 20120303271A1
• Filed: 05/25/2011
• Published: 11/29/2012
• Est. Priority Date: 05/25/2011
• Status: Active Grant

• Current Assignee: CSR Technology Holdings Inc. (Qualcomm, Inc.)
• Original Assignee: SiRF Technology Holdings Incorporated (Qualcomm, Inc.)
• Inventors: Chowdhary, Mahesh, Sharma, Manish, Kumar, Arun, Gupta, Anuraag, Agrawal, Prateek
• Granted Patent: US 10,145,707 B2
• Time in Patent Office: Days
• US Class Current: 701/433
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• 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 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/7242 using integration
• CPC Class Codes: G01C 21/183 Compensation of inertial measurements, e.g. for temperature effects
• CPC Class Codes: G01C 22/006 Pedometers
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

The present invention is related to detecting location of a navigation device using sensor data analysis, where the sensor is coupled to the navigation device. A hierarchical algorithm is used for making a series of decisions regarding the location of the navigation device, with each decision corresponding to a class among a plurality of classes related to the possible motion modes and/or precise location of the device, including the location of the device with respect to a person'"'"'s body. By accurately identifying the device location, the hierarchical algorithm facilitates in providing relevant contextual information, thereby enhancing situational awareness.

Claims

1. A method for determining position information about a device, the method comprising:

performing a first level of classification to detect a motion mode of the device by distilling data collected from one or more sensors coupled to the device; and

performing a second level of classification to detect a location of the device on a user'"'"'s body by further distilling data collected from the one or more sensors.

2. The method of claim 1, wherein the first and second levels of classification are based on one or both of time-domain analysis and frequency-domain analysis of the data collected from the one or more sensors.

3. The method of claim 1, wherein a time-domain classification used in one or both of the first level and the second level of classification use classifiers of data collected from the one or more sensors.

4. The method of claim 3, wherein the classifiers include one or more of standard deviation of acceleration, mean of norm of acceleration, zero crossings in the norm of acceleration, number of peaks in the norm of acceleration, amplitude of pitch variation, amplitude of yaw variation, and amplitude of roll variation.

5. The method of claim 1, wherein detecting the motion mode of the device includes:

comparing the collected data from the one or more sensors to two or more sets of training data, each set of training data in the two or more sets of training data corresponding to a motion mode in a plurality of motion modes;

determining, based on the comparison, the current motion mode of the mobile device to be a particular motion mode in the plurality of motion modes.

6. The method of claim 5, wherein comparing the collected data to two or more sets of training data comprises computing a set of linear predictor coefficients (LPC) for the collected movement data.

7. The method of claim 6, wherein comparing the collected sensor data to two or more sets of training data further comprises:

converting the LPC to linear spectral frequencies (LSF) for the collected sensor data; and

calculating a spectral distortion between the LSF for the collected sensor data and a LSF for each set of training data in the two or more sets of training data.

8. The method of claim 7, wherein determining, based on the comparison, the motion mode of the device to be a particular motion mode in the plurality of motion modes comprises:

determining the particular motion mode to be the motion mode whose corresponding set of training data has LSF that have a least amount of spectral distortion with the LSF of the collected sensor data.

9. The method of claim 5, wherein the plurality of motion modes includes two or more of:

stationary, walking, fast walking, jogging, climbing up/down stairs, going up/down on an escalator, going up/down on an elevator, biking, driving, and riding in a vehicle.

10. The method of claim 1, wherein the detected location is used to select a dead-reckoning (DR) positioning algorithm.

11. The method of claim 5, wherein the plurality of motion modes includes a non-stationary motion mode, the method further comprising:

in response to determining the motion mode to be the non-stationary motion mode, deciding to perform the second level of classification.

12. The method of claim 1, wherein the second level of classification includes:

based on the detected motion mode, deciding to determine an orientation of the device.

13. The method of claim 12, wherein the detected motion mode is walking or fast walking.

14. The method of claim 12, wherein if a first orientation is determined, deciding to determine whether the device is located near the user'"'"'s head, in the user'"'"'s hand, or in a bag carried by the user.

15. The method of claim 14, wherein if a second orientation is determined, deciding to determine whether the device is located in the user'"'"'s trousers, near the user'"'"'s waist, in the user'"'"'s hand, in the user'"'"'s shirt, or in a bag carried by the user.

16. The method of claim 1, wherein the second level of classification includes:

based on the detected motion mode, deciding to determine a head or trouser location of the device.

17. The method of claim 14, wherein the detected motion mode is jogging or running.

18. The method of claim 1, wherein the sensors comprise an inertial measurement unit (IMU).

19. An apparatus that comprises circuitry that performs the method of claim 1.

20. An apparatus that comprises circuitry that performs the method of claim 5.

21. A location positioning apparatus for a device, comprising:

an input coupled to receive data from an inertial motion unit (IMU) of the device;

a processor that determines, based on a first level of classification using the received data, the current motion mode of the mobile device to be a particular motion mode in a plurality of motion mode, and

further determines, based on a second level of classification using the received data, the current location of the device with respect to a user'"'"'s body.