초록 ▼

Techniques are provided which may be implemented using various methods and/or apparatuses in a mobile device to classify and/or otherwise determine a “motion state” of the mobile device. The mobile device may, for example, classify a motion state of the mobile device based on sensed data (e.g., from

Techniques are provided which may be implemented using various methods and/or apparatuses in a mobile device to classify and/or otherwise determine a “motion state” of the mobile device. The mobile device may, for example, classify a motion state of the mobile device based on sensed data (e.g., from inertial sensors, environmental sensors, etc.) that may be filtered based on a vibration profile. A motion state may then affect operation of one or more other functions performed or supported by the mobile device.

대표청구항 ▼

1. A method for use in classifying a motion state of a mobile device, the method comprising: at a mobile device: filtering sensed data from one or more sensors at said mobile device based, at least in part, on a vibration profile for at least one mechanism capable of inducing movement of at least sa

1. A method for use in classifying a motion state of a mobile device, the method comprising: at a mobile device: filtering sensed data from one or more sensors at said mobile device based, at least in part, on a vibration profile for at least one mechanism capable of inducing movement of at least said mobile device;determining a variance value corresponding to a change over a period of time for at least a portion of said filtered sensed data; andclassifying a motion state of said mobile device based, at least in part, on a comparison of said variance value with at least one threshold value. 2. The method as recited in claim 1, wherein said variance value represents at least one of: an average variance value of said portion of said filtered sensed data during said period of time, or a maximum variance value of said portion of said filtered sensed data during said period of time. 3. The method as recited in claim 1, wherein said period of time corresponds to said vibration profile. 4. The method as recited in claim 1, further comprising: selectively obtaining said sensed data in response to an initial motion detection event. 5. The method as recited in claim 1, wherein said motion state comprises either a stationary state or at least one type of non-stationary state. 6. The method as recited in claim 1, wherein classifying said motion state further comprises: classifying said motion state based, at least in part, on at least one satellite positioning system (SPS) signal obtained at said mobile device. 7. The method as recited in claim 1, further comprising: affecting operation of at least one function performed, at least in part, by said mobile device based, at least in part, on said motion state. 8. The method as recited in claim 7, wherein said at least one function performed, at least in part, by said mobile device comprises at least one of a navigation function and/or a power control function. 9. The method as recited in claim 7, wherein said at least one mechanism comprises at least one of a mobile device user and/or a machine. 10. An apparatus for use in classifying a motion state of a mobile device, the apparatus comprising: memory; andat least one processing unit to: obtain sensed data from said memory, said sensed data corresponding to one or more sensors at a mobile device;filter said sensed data based, at least in part, on a vibration profile for at least one mechanism capable of inducing movement of at least said mobile device;determine a variance value corresponding to a change over a period of time for at least a portion of said filtered sensed data; anddetermine a motion state of said mobile device based, at least in part, on a comparison of said variance value with at least one threshold value. 11. The apparatus as recited in claim 10, wherein said variance value represents at least one of: an average variance value of said portion of said filtered sensed data during said period of time, or a maximum variance value of said portion of said filtered sensed data during said period of time. 12. The apparatus as recited in claim 10, wherein said period of time corresponds to said vibration profile. 13. The apparatus as recited in claim 10, said at least one processing unit to further: selectively obtain said sensed data in response to an initial motion detection event. 14. The apparatus as recited in claim 10, wherein said motion state comprises either a stationary state or at least one type of non-stationary state. 15. The apparatus as recited in claim 10, further comprising: a satellite positioning system (SPS) receiver; andwherein said at least one processing unit to further determine said motion state based, at least in part, on at least one SPS signal obtained at said mobile device via said SPS receiver. 16. The apparatus as recited in claim 10, said at least one processing unit to further: affect operation of at least one function performed, at least in part, by said mobile device based, at least in part, on said motion state. 17. The apparatus as recited in claim 16, wherein said at least one function performed, at least in part, by said mobile device comprises at least one of a navigation function and/or a power control function. 18. The apparatus as recited in claim 16, wherein said at least one mechanism comprises at least one of a mobile device user and/or a machine. 19. An apparatus for use in classifying a motion state of a mobile device, the apparatus comprising: means for filtering sensed data from one or more sensors at a mobile device based, at least in part, on a vibration profile for at least one mechanism capable of inducing movement of at least said mobile device;means for determining a variance value corresponding to a change over a period of time for at least a portion of said filtered sensed data; andmeans for classifying a motion state of said mobile device based, at least in part, on a comparison of said variance value with at least one threshold value. 20. The apparatus as recited in claim 19, wherein said variance value represents at least one of: an average variance value of said portion of said filtered sensed data during said period of time, or a maximum variance value of said portion of said filtered sensed data during said period of time. 21. The apparatus as recited in claim 19, wherein said period of time corresponds to said vibration profile. 22. The apparatus as recited in claim 19, further comprising: means for selectively obtaining said sensed data in response to an initial motion detection event. 23. The apparatus as recited in claim 19, wherein said motion state comprises either a stationary state or at least one type of non-stationary state. 24. The apparatus as recited in claim 19, wherein said means for classifying said motion state further comprises: means for classifying said motion state based, at least in part, on at least one satellite positioning system (SPS) signal obtained at said mobile device. 25. The apparatus as recited in claim 19, further comprising: means for affecting operation of at least one function performed, at least in part, by said mobile device based, at least in part, on said motion state. 26. The apparatus as recited in claim 25, wherein said at least one function performed, at least in part, by said mobile device comprises at least one of a navigation function and/or a power control function. 27. The apparatus as recited in claim 25, wherein said at least one mechanism comprises at least one of a mobile device user and/or a machine. 28. An article comprising: a non-transitory computer readable medium having stored therein computer-implementable instructions executable by one or more processing units of a mobile device to: obtain sensed data corresponding to one or more sensors at said mobile device;filter said sensed data based, at least in part, on a vibration profile for at least one mechanism capable of inducing movement of at least said mobile device;determine a variance value corresponding to a change over a period of time for at least a portion of said filtered sensed data; anddetermine a motion state of said mobile device based, at least in part, on a comparison of said variance value with at least one threshold value. 29. The article as recited in claim 28, wherein said variance value represents at least one of: an average variance value of said portion of said filtered sensed data during said period of time, or a maximum variance value of said portion of said filtered sensed data during said period of time. 30. The article as recited in claim 28, wherein said period of time corresponds to said vibration profile. 31. The article as recited in claim 28, wherein said computer-implementable instructions are further executable by said one or more processing units to: selectively obtain said sensed data in response to an initial motion detection event. 32. The article as recited in claim 28, wherein said motion state comprises either a stationary state or at least one type of non-stationary state. 33. The article as recited in claim 28, wherein said computer-implementable instructions are further executable by said one or more processing units to: determine said motion state based, at least in part, on at least one satellite positioning system (SPS) signal obtained at said mobile device. 34. The article as recited in claim 28, wherein said computer-implementable instructions are further executable by said one or more processing units to: affect operation of at least one function performed, at least in part, by said mobile device based, at least in part, on said motion state. 35. The article as recited in claim 34, wherein said at least one function performed, at least in part, by said mobile device comprises at least one of a navigation function and/or a power control function. 36. The article as recited in claim 34, wherein said at least one mechanism comprises at least one of a mobile device user and/or a machine.