DEVICES, METHODS, AND APPARATUSES FOR INFERRING A POSITION OF A MOBILE DEVICE

• US 20130029681A1
• Filed: 01/31/2012
• Published: 01/31/2013
• Est. Priority Date: 03/31/2011
• Status: Abandoned Application

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Grokop, Leonard Henry
• Application Number:
  US13/362,485
• Publication Number:
  US 20130029681A1
• Time in Patent Office: Days
• US Class Current: 455/456.1
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G01C 22/006 Pedometers
• 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: G06F 1/1694 the I/O peripheral being a single or a set of motion sensors for pointer control or gesture input obtained by sensing movements of the portable computer

Abstract

Components, methods, and apparatuses are provided that may be used to characterize a spectral envelope of at least one signal received from one or more inertial sensors of a mobile device co-located with a user engaged in an activity and to infer a position of the mobile device with respect to the user engaged in an activity based, at least in part, on the characterization of the spectral envelope.

Claims

1. A method comprising:

determining one or more parameters characterizing a spectral envelope of at least one signal received from one or more inertial sensors of a mobile device co-located with a user engaged in an activity; and

inferring a position state of said mobile device based, at least in part, on said characterization of said spectral envelope.

2. The method of claim 1, wherein inferring said position state comprises inferring said position state from a plurality of candidate position states using a Bayesian classifier.

3. The method of claim 1, wherein inferring said position state comprises inferring said position state from a plurality of candidate position states with respect to a user comprising at least one of:

being in said user'"'"'s hand,

being fastened to said user'"'"'s wrist or arm while said user is walking, running, or riding a bicycle,

being in said user'"'"'s shirt or coat pocket while said user is walking, running, or riding a bicycle or a motorcycle,

being in said user'"'"'s pants pocket while said user is walking, running, or riding a bicycle,

being in a holster attached to said user'"'"'s belt or clothing,

being in a bag, suitcase, or briefcase carried or wheeled by said user, and

being in an automobile, a bus, or a train.

4. The method of claim 3, further comprising:

inferring that said user is walking with said mobile device in said user'"'"'s hand based, at least in part, on detecting acceleration of said mobile device in one direction, said acceleration in said one direction being greater than acceleration in at least second and third directions.

5. The method of claim 3, further comprising:

inferring that said user is walking with said mobile device in said user'"'"'s pocket based, at least in part, on detecting acceleration peaks in a first direction, said acceleration peaks being greater than acceleration peaks in second and third directions.

6. The method of claim 1, wherein said determining one or more parameters characterizing a spectral envelope further comprises:

computing Cepstral Coefficients based, at least in part, on said at least one signal.

7. The method of claim 1, wherein said determining one or more parameters characterizing a spectral envelope comprises performing one or more computations selected from the group consisting of:

computing Mel-Frequency Cepstral Coefficients, computing delta Cepstral Coefficients, computing delta Mel-Frequency Cepstral Coefficients, computing accel Cepstral Coefficients, computing accel Mel-Frequency Cepstral Coefficients, computing Linear Prediction Coefficients, computing delta Linear Prediction coefficients, and computing accel linear prediction coefficients,

based, at least in part, on said at least one signal.

8. The method of claim 1, further comprising:

measuring a pitch of said at least one signal; and

inferring said position state based, at least in part, on said measured pitch.

9. The method of claim 1, and further comprising:

measuring a spectral entropy of said at least one signal; and

inferring said position state based, at least in part, on said measured spectral entropy.

10. The method of claim 1, and further comprising:

measuring a zero crossing rate of said at least one signal; and

inferring said position state based, at least in part, on said measured Zero Crossing Rate.

11. The method of claim 1, and further comprising:

measuring spectral centroid of said at least one signal; and

inferring said position state based, at least in part, on said measured spectral centroid.

12. The method of claim 1, and further comprising:

measuring a bandwidth of said at least one signal; and

inferring said position state based, at least in part, on said measured bandwidth.

13. The method of claim 1, and further comprising:

measuring band energies of said at least one signal; and

inferring said position state based, at least in part, on said measured band energies.

14. The method of claim 1, and further comprising:

measuring a spectral flux of said at least one signal; and

inferring said position state based, at least in part, on said measured spectral flux.

15. The method of claim 1, and further comprising:

measuring a spectral roll-off of said at least one signal; and

inferring said position state based, at least in part, on said measured spectral roll-off.

16. An apparatus comprising:

means for sensing movement of a mobile device;

means for characterizing a spectral envelope of at least one signal received from said means for sensing movement; and

means for inferring a position state of said mobile device with respect to said user based, at least in part, on said characterization of said spectral envelope.

17. The apparatus of claim 16, further comprising means for inferring an activity of the user based, at least in part, on said characterization of said spectral envelope.

18. The apparatus of claim 17, wherein said means for characterizing further comprises:

means for computing Cepstral Coefficients based, at least in part, on said at least one signal.

19. An article comprising:

a non-transitory storage medium comprising machine-readable instructions stored thereon which are executable by a processor of a mobile device to;

characterize a spectral envelope of at least one signal received from one or more inertial sensors of a mobile device; and

infer a position state of said mobile device with respect to said user engaged in an activity based, at least in part, on said characterization of said spectral envelope.

20. A mobile device comprising:

one or more inertial sensors for measuring motion of said mobile device; and

one or more processors to;

characterize a spectral envelope of at least one signal received from said one or more inertial sensors; and

infer a position state of said mobile device with respect to said user engaged in an activity based, at least in part, on said characterizing of said spectral envelope.

21. The mobile device of claim 20, wherein said one or more processors further infers said position state of said mobile device with respect to said user from a plurality of candidate position states with respect to said user comprising at least one of:

being in said user'"'"'s hand, being fastened to said user'"'"'s wrist or arm, being in said user'"'"'s shirt, coat, or pants pocket, or being in said user'"'"'s bag while said user is engaged in an activity.

22. The mobile device of claim 21, wherein said one or more processors further classifies said activity from a plurality of candidate activities consisting of:

walking, running, riding a bicycle, and riding in an automobile, riding in a bus, riding in a train, or riding on a motorcycle.

23. The mobile device of claim 21, wherein said one or more processors further computes Cepstral Coefficients based, at least in part, on said at least one signal.