GYRO AIDED TAP GESTURE DETECTION

• US 20140168057A1
• Filed: 05/06/2013
• Published: 06/19/2014
• Est. Priority Date: 12/13/2012
• Status: Abandoned Application

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Puig, Carlos M., Joshi, Ashutosh, Ahuja, Disha
• Application Number:
  US13/887,695
• Publication Number:
  US 20140168057A1
• Time in Patent Office: Days
• US Class Current: 345/156
• CPC Class Codes: G06F 1/1626 with a single-body enclosure integrating a flat display, e.g. Personal Digital Assistants [PDAs]
• 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
• CPC Class Codes: G06F 2200/1636 Sensing arrangement for detection of a tap gesture on the housing
• CPC Class Codes: G06F 3/017 Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06F 3/0346 with detection of the device orientation or free movement in a 3D space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors

Abstract

Methods, systems, computer-readable media, and apparatuses for tap detection in a mobile device are presented. In some embodiments, the method may comprise storing, by a mobile device, a first data sample from an accelerometer sensor and a second data sample from a gyroscope sensor. Additionally, the method may comprise processing a plurality of data samples. The plurality of data samples can include the first data sample or the second data sample. Optionally, in one embodiment, the method may comprise suppressing a tap that has been classified as a false detection based on at least one of the plurality of data samples. Subsequently, the method may comprise determining an occurrence of a tap at a mobile device based on the results of the processing.

Claims

1. A method, comprising:

receiving a first data sample from an accelerometer sensor;

receiving a second data sample from a gyroscope sensor;

processing a plurality of data samples, wherein the plurality of data samples includes the first data sample or the second data sample; and

determining an occurrence of a tap at a mobile device based on the results of the processing.

2. The method of claim 1, wherein the determining further comprises:

suppressing a tap that has been classified as a false detection based on at least one of the plurality of data samples.

3. The method of claim 2, wherein the suppressing further comprises:

detecting, based on the second data sample from the gyroscope sensor, an orientation change within a time threshold of a potential detection of a tap; and

suppressing the tap associated with the false detection classification based on the detected orientation change.

4. The method of claim 2, wherein the plurality of data samples further includes a gesture recognition data sample, and the suppressing is further based on the gesture recognition data sample.

5. The method of claim 1, wherein the processing further comprises:

calculating a windowed jerk, wherein the windowed jerk is the difference between the maxima and minima of the first data sample from the accelerometer sensor.

6. The method of claim 1, wherein the processing further comprises:

calculating an angular acceleration, wherein the an angular acceleration is the difference between the maxima and minima of the second data sample from the gyroscope sensor.

7. The method of claim 1, wherein the determining further comprises:

determining a minimum signal strength; and

comparing the first data sample with the minimum signal strength to determine whether a tap has occurred.

8. The method of claim 1, wherein the determining further comprises:

determining a minimum signal strength; and

comparing the second data sample with the minimum signal strength to determine whether a tap has occurred.

9. The method of claim 1, wherein the determining further comprises:

calculating a signal-to-noise ratio; and

comparing the first data sample with the signal-to-noise ratio to determine whether a tap has occurred.

10. The method of claim 9, wherein the signal-to-noise ratio is a jerk magnitude from an axis divided by a jerk magnitude from one or more other axes.

11. The method of claim 1, wherein the determining further comprises:

calculating a signal-to-noise ratio; and

comparing the output of the processing of the second data sample with the signal-to-noise ratio to determine whether a tap is detected.

12. The method of claim 11, wherein the signal-to-noise ratio is an angular acceleration magnitude from an axis divided by an angular acceleration magnitude from one or more other axes.

13. The method of claim 1, wherein the results of the processing include an axis of motion of the tap based on the first and second data samples.

14. The method of claim 1, wherein the results of the processing includes a sign of motion of the tap based on the first data sample.

15. The method of claim 1, wherein the detection of a tap comprises a left tap, a right tap, a top tap and a bottom tap detection relative to a front surface of the mobile device.

16. The method of claim 1, wherein the detection of a tap comprises ignoring a front and a back tap detection relative to a front surface of the mobile device.

17. The method of claim 1, wherein a representation of the direction of the tap is sent to an application, wherein the application uses the direction as a user input.

18. A device comprising:

one or more processors;

memory storing computer-readable instructions that, when executed by the one or more processors, cause the device to;

receive a first data sample from an accelerometer sensor;

receive a second data sample from a gyroscope sensor;

process a plurality of data samples, wherein the plurality of data samples includes the first data sample or the second data sample; and

determine an occurrence of a tap at a mobile device based on the results of the processing.

19. The device of claim 18, further comprising computer-readable instructions that, when executed by the one or more processors, cause the device to:

suppress a tap that has been classified as a false detection based on at least one of the plurality of data samples.

20. The device of claim 19, further comprising computer-readable instructions that, when executed by the one or more processors, cause the device to:

detect, based on the second data sample from the gyroscope sensor, an orientation change within a time threshold of a potential detection of a tap; and

suppress the tap associated with the false detection classification based on the detected orientation change.

21. The device of claim 19, wherein the plurality of data samples further includes a gesture recognition data sample, and the suppressing is further based on the gesture recognition data sample.

22. The device of claim 18, further comprising computer-readable instructions that, when executed by the one or more processors, cause the device to:

calculate a windowed jerk, wherein the windowed jerk is the difference between the maxima and minima of the first data sample from the accelerometer sensor.

23. The device of claim 18, further comprising computer-readable instructions that, when executed by the one or more processors, cause the device to:

calculate an angular acceleration, wherein the angular acceleration is the difference between the maxima and minima of the second data sample from the gyroscope sensor.

24. The device of claim 18, further comprising computer-readable instructions that, when executed by the one or more processors, cause the device to:

determine a minimum signal strength; and

compare the first data sample with the minimum signal strength to determine whether a tap has occurred.

25. The device of claim 18, further comprising computer-readable instructions that, when executed by the one or more processors, cause the device to:

determine a minimum signal strength; and

compare the second data sample with the minimum signal strength to determine whether a tap has occurred.

26. The device of claim 18, further comprising computer-readable instructions that, when executed by the one or more processors, cause the device to:

calculate a signal-to-noise ratio; and

compare the first data sample with the signal-to-noise ratio to determine whether a tap has occurred.

27. The device of claim 26, wherein the signal-to-noise ratio is a jerk magnitude from an axis divided by a jerk magnitude from one or more other axes.

28. The device of claim 18, further comprising computer-readable instructions that, when executed by the one or more processors, cause the device to:

calculate a signal-to-noise ratio; and

compare the output of the processing the second data sample with the signal-to-noise ratio to determine whether a tap is detected.

29. The device of claim 28, wherein the signal-to-noise ratio is an angular acceleration magnitude from an axis divided by an angular acceleration magnitude from one or more other axes.

30. The device of claim 18, wherein the results of the processing include an axis of motion of the tap based on the first and second data samples.

31. The device of claim 18, wherein the results of the processing includes a sign of motion of the tap based on the first data sample.

32. The device of claim 18, wherein the detection of a tap comprises a left tap, a right tap, a top tap and a bottom tap detection relative to a front surface of the mobile device.

33. The device of claim 18, wherein the detection of a tap comprises ignoring a front and a back tap detection relative to a front surface of the mobile device.

34. The device of claim 18, wherein a representation of the direction of the tap is sent to an application, wherein the application uses the direction as a user input.

35. One or more computer-readable media storing computer-executable instructions for detecting a tab in a mobile device that, when executed, cause one or more computing devices included in the mobile device to:

receive a first data sample from an accelerometer sensor;

receive a second data sample from a gyroscope sensor;

process a plurality of data samples, wherein the plurality of data samples includes the first data sample or the second data sample; and

determine an occurrence of a tap at a mobile device based on the results of the processing.

36. An apparatus for detecting a tap in a mobile device, the apparatus comprising:

means for receiving a first data sample from an accelerometer sensor;

means for receiving a second data sample from a gyroscope sensor;

means for processing a plurality of data samples, wherein the plurality of data samples includes the first data sample or the second data sample;

means for determining an occurrence of a tap at a mobile device based on the results of the processing.

37. The apparatus of claim 36, wherein the means for determining further comprises:

means for suppressing a tap that has been classified as a false detection based on at least one of the plurality of data samples.

38. The apparatus of claim 37, wherein the suppressing further comprises:

means for detecting, based on the second data sample from the gyroscope sensor, an orientation change within a time threshold of a potential detection of a tap; and

means for suppressing the tap associated with the false detection classification based on the detected orientation change.

39. The apparatus of claim 37, wherein the plurality of data samples further includes a gesture recognition data sample, and the suppressing is further based on the gesture recognition data sample.

40. The apparatus of claim 36, wherein the means for processing further comprises:

means for calculating a windowed jerk, wherein the windowed jerk is the difference between the maxima and minima of the first data sample from the accelerometer sensor.

41. The apparatus of claim 36, wherein the processing further comprises:

calculating an angular acceleration, wherein the angular acceleration is the difference between the maxima and minima of the second data sample from the gyroscope sensor.

42. The apparatus of claim 36, wherein the determining further comprises:

determining a minimum signal strength; and

comparing the first data sample with the minimum signal strength to determine whether a tap has occurred.

43. The apparatus of claim 36, wherein the determining further comprises:

determining a minimum signal strength; and

comparing the second data sample with the minimum signal strength to determine whether a tap has occurred.

44. The apparatus of claim 36, wherein the determining further comprises:

calculating a signal-to-noise ratio; and

comparing the first data sample with the signal-to-noise ratio to determine whether a tap has occurred.

45. The apparatus of claim 44, wherein the signal-to-noise ratio is a jerk magnitude from an axis divided by a jerk magnitude from one or more other axes.

46. The apparatus of claim 36, wherein the determining further comprises:

calculating a signal-to-noise ratio; and

comparing the output of the processing of the second data sample with the signal-to-noise ratio to determine whether a tap is detected.

47. The apparatus of claim 46, wherein the signal-to-noise ratio is an angular acceleration magnitude from an axis divided by an angular acceleration magnitude from one or more other axes.

48. The apparatus of claim 36, wherein the results of the processing includes an axis of motion of the tap based on the first and second data samples.

49. The apparatus of claim 36, wherein the results of the processing includes a sign of motion of the tap based on the first data sample.

50. The apparatus of claim 36, wherein the detection of a tap comprises a left tap, a right tap, a top tap and a bottom tap detection relative to a front surface of the mobile device.

51. The apparatus of claim 36, wherein the detection of a tap comprises ignoring a front and a back tap detection relative to a front surface of the mobile device.

52. The apparatus of claim 36, wherein a representation of the direction of the tap is sent to an application, wherein the application uses the direction as a user input.