Adaptive sensor sampling for power efficient context aware inferences

Patent metadata

Field	Value
Publication number	US-9268399-B2
Application number	US-201313782989-A
Country	US
Kind code	B2
Filing date	Mar 1, 2013
Priority date	Mar 1, 2013
Publication date	Feb 23, 2016
Grant date	Feb 23, 2016

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.

First claim

What is claimed is:

1. A method for performing context inference for a mobile device comprising:
receiving a data sample from at least a first sensor;
performing a first classification of the data sample from the first sensor;
determining a confidence value for the first classification, wherein determining the confidence value comprises:
detecting a change in classification from a prior data sample classification, of a prior data sample from the first sensor, to the first data sample classification, wherein the change in classification matches a change in classification predetermined to trigger a confidence failure;

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 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.

5. 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.

6. 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 a data sample from at least a first sensor;
performing a first classification of the data sample from the first sensor;
determining a confidence value for the first classification, wherein determining the confidence value comprises:
detecting a change in classification from a prior data sample classification, of a prior data sample from the first sensor, to the first data sample classification, wherein the change in classification matches a change in classification predetermined to trigger a confidence failure;
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.

7. The machine readable non-transitory storage medium of claim 6 , 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.

8. The machine readable non-transitory storage medium of claim 6 , wherein receiving data from at least a first sensor comprises:
receiving data from two or more sensors.

9. The machine readable non-transitory storage medium of claim 6 , 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.

10. The machine readable non-transitory storage medium of claim 6 , 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.

11. 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 a data sample from at least a first sensor;
perform a first classification of the data sample from the first sensor;
determine a confidence value for the first classification, wherein to determine the confidence value comprises:
detect a change in classification from a prior data sample classification, of a prior data sample from the first sensor, to the first data sample classification, wherein the change in classification matches a change in classification predetermined to trigger a confidence failure;

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.

12. The data processing device of claim 11 , 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.

13. The data processing device of claim 11 , wherein receiving data from at least a first sensor further comprises instructions to:
receive data from two or more sensors.

14. The data processing device of claim 11 , 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.

15. The data processing device of claim 11 , 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.

16. 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 a data sample from the first sensor;
means for determining a confidence value for the first classification, wherein determining the confidence value comprises:
detecting a change in classification from a prior data sample classification, of a prior data sample from the first sensor, to the first data sample classification, wherein the change in classification matches a change in classification predetermined to trigger a confidence failure;

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.

17. The apparatus of claim 16 , 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 performin

Assignees

• Qualcomm Inc

Inventors

• Grokop Leonard Henry
• Sadasivam Shankar

Classifications

• G06F1/3287

  by switching off individual functional units in the computer system · CPC title

• G06F3/011 Primary

  Arrangements for interaction with the human body, e.g. for user immersion in virtual reality (blind teaching G09B21/00) · CPC title

• Y02B60/1282

  Cross-Sectional Technologies · mapped topic

• H04M1/72569

  Electricity · mapped topic

• H04M1/72454

  according to context-related or environment-related conditions · CPC title