ADAPTIVE SENSOR SAMPLING FOR POWER EFFICIENT CONTEXT AWARE INFERENCES

• US 20140247206A1
• Filed: 03/01/2013
• Published: 09/04/2014
• Est. Priority Date: 03/01/2013
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Grokop, Leonard Henry, Sadasivam, Shankar
• Granted Patent: US 9,268,399 B2
• Time in Patent Office: Days
• US Class Current: 345/156
• CPC Class Codes: G06F 1/3287 by switching off individual functional units in the computer system
• CPC Class Codes: G06F 3/011 Arrangements for interaction with the human body, e.g. for user immersion in virtual reality blind teaching G09B21/00
• CPC Class Codes: H04M 1/72454 according to context-related or environment-related conditions
• CPC Class Codes: Y02D 10/00 Energy efficient computing, e.g. low power processors, power management or thermal management
• CPC Class Codes: Y02D 30/70 in wireless communication networks

Abstract

Disclosed is a system, apparatus, computer readable storage medium, and method to perform a context inference for a mobile device. In one embodiment, a data processing system includes a processor and a storage device configurable to store instructions to perform a context inference for the data processing system. Data may be received from at least a first sensor, and a first classification of the data from the sensor may be performed. Confidence for the first classification can be determined and a second sensor can be activated based on a determination that the confidence fails to meet a confidence threshold. A data sample classification from the activated second sensor may be classified jointly with the data from first sensor

Claims

1. A method for performing context inference for a mobile device comprising:

receiving data from at least a first sensor;

performing a first classification of the data from the at least first sensor;

determining a confidence value for the first classification;

activating a second sensor based on a determination the confidence value of the first classification fails to meet a confidence threshold; and

performing a second classification of a second data sample from the activated second sensor jointly with the data from the at least first sensor.

2. The method of claim 1, further comprising:

determining a confidence value for the second classification;

activating a third sensor based on a determination that the confidence value fails to meet a confidence threshold; and

performing a third classification of a third data sample.

3. The method of claim 1, wherein receiving data from at least a first sensor comprises:

receiving data from two or more sensors.

4. The method of claim 1, wherein the determining the confidence value further comprises:

detecting a change in classification from a prior data sample classification to the first data sample classification.

5. The method of claim 4, wherein the change in classification matches a change in classification predetermined to trigger a confidence failure.

6. The method of claim 1, wherein the confidence value is based on at least one of:

a probability of correctly classifying the first data sample, a comparison of the first data sample to a data sample in a historical data set, and a difference between a nearest classification and a next nearest classification.

7. The method of claim 1, wherein the first sensor is part of a first sensor subsystem and the second sensor is part of a second sensor subsystem, and wherein activating the second sensor further comprises:

powering up the second sensor subsystem, and

communicating between the first and second sensor subsystems.

8. A machine readable non-transitory storage medium containing executable program instructions which cause a mobile device to perform a method for performing context inference for the mobile device, the method comprising:

receiving data from at least a first sensor;

performing a first classification of the data from the at least first sensor;

determining a confidence value for the first classification;

activating a second sensor based on a determination the confidence value of the first classification fails to meet a confidence threshold; and

performing a second classification of a second data sample from the activated second sensor jointly with the data from the at least first sensor.

9. The machine readable non-transitory storage medium of claim 8, further comprising:

determining a confidence value for the second classification;

activating a third sensor based on a determination that the confidence value fails to meet a confidence threshold; and

performing a third classification of a third data sample.

10. The machine readable non-transitory storage medium of claim 8, wherein receiving data from at least a first sensor comprises:

receiving data from two or more sensors.

11. The machine readable non-transitory storage medium of claim 8, wherein the determining the confidence value further comprises:

detecting a change in classification from a prior data sample classification to the first data sample classification.

12. The machine readable non-transitory storage medium of claim 11, wherein the change in classification matches a change in classification predetermined to trigger a confidence failure.

13. The machine readable non-transitory storage medium of claim 8, wherein the confidence value is based on at least one of:

a probability of the classification occurring, a comparison of the first data sample to a data sample in a historical data set, and a difference between a nearest classification and a next nearest classification.

14. The machine readable non-transitory storage medium of claim 8, wherein the first sensor is part of a first sensor subsystem and the second sensor is part of a second sensor subsystem, and wherein activating the second sensor further comprises:

powering up the second sensor subsystem, and

communicating between the first and second sensor subsystems.

15. A data processing device comprising:

a processor;

a storage device coupled to the processor and configurable for storing instructions, which, when executed by the processor cause the processor to;

receive data from at least a first sensor;

perform a first classification of the data from the at least first sensor;

determine a confidence value for the first classification;

activate a second sensor based on a determination the confidence value of the first classification fails to meet a confidence threshold; and

perform a second classification of a second data sample from the activated second sensor jointly with the data from the at least first sensor.

16. The data processing device of claim 15, further comprising instructions to:

determine a confidence value for the second classification;

activate a third sensor based on a determination that the confidence value fails to meet a confidence threshold; and

perform a third classification of a third data sample.

17. The data processing device of claim 15, wherein receiving data from at least a first sensor further comprises instructions to:

receive data from two or more sensors.

18. The data processing device of claim 15, wherein the determining the confidence value further comprises instructions to:

detect a change in classification from a prior data sample classification to the first data sample classification.

19. The data processing device of claim 18, wherein the change in classification matches a change in classification predetermined to trigger a confidence failure.

20. The data processing device of claim 15, wherein the confidence value is based on at least one of:

a probability of the classification occurring, a comparison of the first data sample to a data sample in a historical data set, and a difference between a nearest classification and a next nearest classification.

21. The data processing device of claim 15, wherein the first sensor is part of a first sensor subsystem and the second sensor is part of a second sensor subsystem, and wherein activating the second sensor further comprises instructions to:

power up the second sensor subsystem, and

communicate between the first and second sensor subsystems.

22. An apparatus for performing context inference for a mobile device comprising:

means for receiving data from at least a first sensor;

means for performing a first classification of the data from the at least first sensor;

means for determining a confidence value for the first classification;

means for activating a second sensor based on a determination the confidence value of the first classification fails to meet a confidence threshold; and

means for performing a second classification of a second data sample from the activated second sensor jointly with the data from the at least first sensor.

23. The apparatus of claim 22, further comprising:

means for determining a confidence value for the second classification;

means for activating a third sensor based on a determination that the confidence value fails to meet a confidence threshold; and

means for performing a third classification of a third data sample.

24. The apparatus of claim 22, wherein receiving data from at least a first sensor comprises:

receiving data from two or more sensors.

25. The apparatus of claim 22, wherein the determining the confidence value further comprises:

means for detecting a change in classification from a prior data sample classification to the first data sample classification.

26. The apparatus of claim 25, wherein the change in classification matches a change in classification predetermined to trigger a confidence failure.

27. The apparatus of claim 22, wherein the confidence value is based on at least one of:

a probability of the classification occurring, a comparison of the first data sample to a data sample in a historical data set, and a difference between a nearest classification and a next nearest classification.

28. The apparatus of claim 22, wherein the first sensor is part of a first sensor subsystem and the second sensor is part of a second sensor subsystem, and wherein activating the second sensor further comprises:

means for powering up the second sensor subsystem, and

means for communicating between the first and second sensor subsystems.