In-transit detection using low complexity algorithm fusion and phone state heuristics

• US 9,730,145 B2
• Filed: 03/15/2013
• Issued: 08/08/2017
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Puig, Carlos M., Ahuja, Disha, Lee, Jin Won, Sadasivam, Shankar
• Primary Examiner(s): Lee, Jae Y
• Assistant Examiner(s): Voltaire, Jean F
• Application Number:
  US13/839,483
• Publication Number:
  US 20140269363A1
• Time in Patent Office: 1,607 Days
• Field of Search: 370252
• CPC Class Codes: G01C 22/006 Pedometers
• CPC Class Codes: G01S 5/017 Detecting state or type of motion
• CPC Class Codes: H04W 4/025 using location based information parameters
• CPC Class Codes: H04W 4/21 for social networking applications
• CPC Class Codes: H04W 48/04 based on user or terminal location or mobility data, e.g. moving direction, speed
• CPC Class Codes: H04W 64/006 with additional information processing, e.g. for direction or speed determination
• CPC Class Codes: Y02D 30/70 in wireless communication networks

Abstract

System and methods are disclosed to use information available on the state of mobile devices in a heuristics-based approach to improve motion state detection. In one or more embodiments, information on the WiFi connectivity of mobile devices may be used to improve the detection of the in-transit state. The WiFi connectivity information may be used with sensor signal such as accelerometer signals in a motion classifier to reduce the false positives of the in-transit state. In one or more embodiments, information that a mobile device is connected to a WiFi access point (AP) may be used as heuristics to reduce the probability of falsely classifying the mobile device in the in-transit state when mobile device is actually in the hand of a relatively stationary user. Information on the battery charging state or the wireless connectivity of the mobile devices may also be used to improve the detection of in-transit state.

Claims

1. A method for detecting a motion state associated with a device, comprising:

receiving information on WiFi connectivity of the device;

determining a WiFi connectivity state based on the information on WiFi connectivity, wherein the WiFi connectivity state indicates whether the device is connected to a mobile WiFi access point, connected to a non-mobile WiFi access point, or not connected to a WiFi access point;

processing the information on WiFi connectivity of the device to set a probability that the device is in a first motion state based on the WiFi connectivity state;

receiving one or more sensor signals from one or more sensor devices, wherein characteristics of the one or more sensor signals reflect motion of the device; and

processing by a motion classifier the one or more sensor signals aided by the probability that the device is in the first motion state to generate a final motion state for the device.

2. The method of claim 1, wherein the processing by the motion classifier further comprises biasing the final motion state away from the first motion state based on a low probability that the device is in the first motion state as set by the processing of the information on WiFi connectivity of the device.

3. The method of claim 2, wherein the processing by the motion classifier further comprises, responsive to determining that the probability that the device is in the first motion state is low, eliminating the first motion state from consideration for the final motion state.

4. The method of claim 1, wherein the processing by the motion classifier further comprises biasing the final motion state toward the first motion state based on a high probability that the device is in the first motion state as set by the processing of the information on WiFi connectivity of the device.

5. The method of claim 1, wherein the processing the information on WiFi connectivity of the device further comprises:

setting a low probability that the device is in an in-transit state if the WiFi connectivity state indicates that the device is connected to a non-mobile WiFi access point.

6. The method of claim 1, wherein the processing the information on WiFi connectivity of the device further comprises:

setting a high probability that the device is in an in-transit state if the WiFi connectivity state indicates that the device is connected to a mobile WiFi access point.

7. The method of claim 1, processing information on a plurality of periodic WiFi scans performed by the device if the WiFi connectivity state indicates that the device is not connected to a WiFi access point.

8. The method of claim 7, further comprising:

determining if there is a common access point in the plurality of periodic WiFi scans; and

setting a low probability that the device is in an in-transit state if there is a common access point in the plurality of periodic WiFi scans.

9. The method of claim 7, wherein the processing the information on WiFi connectivity of the device further comprises:

responsive to an indication that there is no common access point in the plurality of periodic WiFi scans, setting a high probability that the device is in an in-transit state.

10. A method for detecting a motion state associated with a device, comprising:

receiving one or more sensor signals from one or more sensor devices, wherein characteristics of the one or more sensor signals reflect motion of the device;

processing by one or more tuned modules the one or more sensor signals to generate one or more tuned signals, wherein each of the tuned modules is configured to detect a tuned state of the device, and wherein each of the tuned signals indicates a probability of the tuned state, at least one of the one or more tuned signals indicating whether the device is stationary or in motion; and

if the one or more tuned signals indicate that the device is in motion, determining a final motion state of the device based on one or more operating states of the device, the one or more operating states including one or more of;

a charging state of the device; and

a network connectivity of the device.

11. The method of claim 10, wherein one of the tuned modules comprises a pedometer module configured to detect steps.

12. The method of claim 10, wherein the determining of the final motion state of the device further comprises using the probability of the tuned state indicated by one of the tuned signals to drive the final motion state toward the tuned state.

13. The method of claim 10, wherein the determining of the final motion state of the device further comprises bypassing processing by a motion classifier when the probability of the tuned state indicated by one of the tuned signals expresses a high probability of the tuned state.

14. The method of claim 13, wherein when the probability of a stationary state indicated by the tuned signal from one of the tuned modules configured to detect the stationary state exceeds a threshold, the processing by the motion classifier is bypassed, and the final motion state is the stationary state.

15. The method of claim 13, wherein when the probability of a walk state indicated by the tuned signal from one of the tuned modules configured to detect steps exceeds a threshold, the processing by the motion classifier is bypassed, and the final motion state is the walk state.

16. The method of claim 10, wherein the processing by the motion classifier further comprises processing information of the operating state of the device to drive the most likely motion state.

17. The method of claim 16, wherein the processing by the motion classifier further comprises using the information of the operating state of the device to change the probability of one of the one or more tuned states.

18. The method of claim 10, wherein, responsive to a first indication that the device is in a charging state and a second indication that the device is in motion, setting a high probability that the device is in an in-transit state.

19. The method of claim 10, wherein, responsive to a first indication that that the device is connected to a car Bluetooth device or a mobile WiFi access point and a second indication that the device is in motion, setting a high probability that the device is in an in-transit state.

20. The method of claim 10, wherein one of the one or more sensor devices is an accelerometer.

21. An apparatus, comprising:

a sensor configured to register a motion of the apparatus in sensor signals, wherein characteristics of the sensor signals reflect motion of the apparatus;

a memory; and

one or more processors coupled to the memory and configured to;

receive information on WiFi connectivity of the apparatus;

determine a WiFi connectivity state based on the information on WiFi connectivity, wherein the WiFi connectivity state indicates whether the device is connected to a mobile WiFi access point, connected to a non-mobile WiFi access point, or not connected to a WiFi access point;

process the information on WiFi connectivity of the apparatus to set a probability that the apparatus is in a first motion state based on the WiFi connectivity state; and

process the sensor signals aided by the probability that the apparatus is in the first motion state to generate a final motion state for the apparatus.

22. The apparatus of claim 21, wherein the one or more processors are configured to process the sensor signals by being further configured to bias the final motion state away from the first motion state based on a low probability that the apparatus is in the first motion state as processed from the information on WiFi connectivity of the apparatus.

23. The apparatus of claim 22, wherein the one or more processors are configured to process the sensor signals by being further configured to eliminate, responsive to a very low probability that the apparatus is in the first motion state, the first motion state from consideration for the final motion state.

24. The apparatus of claim 21, wherein the one or more processors are configured to process the sensor signals by being further configured to bias the final motion state toward the first motion state based on a high probability that the apparatus is in the first motion state as processed from the information on WiFi connectivity of the apparatus.

25. The apparatus of claim 21, wherein the one or more processors are configured to process the information on WiFi connectivity of the apparatus by being further configured to set a low probability that the apparatus is in an in-transit state if the WiFi connectivity state indicates that the device is connected to a non-mobile WiFi access point.

26. The apparatus of claim 21, wherein the one or more processors are configured to process the information on WiFi connectivity of the apparatus by being further configured to set a high probability that the apparatus is in an in-transit state if the WiFi connectivity state indicates that the device is connected to a mobile WiFi access point.

27. The apparatus of claim 21, wherein the one or more processors are configured to process the information on WiFi connectivity of the apparatus by being further configured to process information on a plurality of periodic WiFi scans performed by the apparatus if the WiFi connectivity state indicates that the device is not connected to a WiFi access point.

28. The apparatus of claim 27, wherein the one or more processors are configured to process the information on WiFi connectivity of the apparatus by being further configured to:

determining if there is a common access point in the plurality of periodic WiFi scans; and

set a low probability that the apparatus is in an in-transit state if there is a common access point in the plurality of periodic WiFi scans.

29. The apparatus of claim 27, wherein the one or more processors are configured to process the information on WiFi connectivity of the apparatus by being further configured to set, responsive to an indication that there is no common Access Point in the plurality of periodic WiFi scans, a high probability that the apparatus is in an in-transit state.

30. An apparatus, comprising:

a sensor configured to register a motion of the apparatus in sensor signals, wherein characteristics of the sensor signals reflect the motion of the apparatus;

a non-transitory memory; and

one or more processors coupled to the non-transitory memory and configured to;

process the sensor signals to generate one or more tuned signals, wherein the one or more processors are further configured to detect one or more tuned states of the apparatus, and wherein each of the tuned signals indicates a probability of one of the tuned states, at least one of the one or more tuned signals indicating whether the device is stationary or in motion; and

if the one or more tuned signals indicate that the device is in motion, determining a final motion state of the apparatus based on one or more operating states of the device, the one or more operating states including one or more of;

a charging state of the device; and

a network connectivity of the device.

31. The apparatus of claim 30, wherein one of the tuned states comprises a stationary state.

32. The apparatus of claim 30, wherein one of the tuned states comprises a walk state.

33. The apparatus of claim 30, wherein the one or more processors are configured to determine the final motion state of the apparatus by being further configured to use the probability of the tuned state indicated by one of the tuned signals to drive the final motion state toward the tuned state.

34. The apparatus of claim 30, wherein the one or more processors are further configured to bypass processing of the sensor signals to generate a most likely motion state when the probability of the tuned state indicated by one of the tuned signals expresses a high probability of the tuned state.

35. The apparatus of claim 34, wherein when the probability of a stationary state indicated by one of the tuned signals for the stationary state exceeds a threshold, the one or more processors are further configured to bypass processing of the sensor signals to generate a most likely motion state, and the final motion state is the stationary state.

36. The apparatus of claim 34, wherein when the probability of a walk state indicated by one of the tuned signals for the walk state exceeds a threshold, the one or more processors are further configured to bypass the process the sensor signals to generate a most likely motion state, and the final motion state is the walk state.

37. The apparatus of claim 30, wherein the one or more processors are further configured to process information of the operating state of the apparatus to drive the most likely motion state.

38. The apparatus of claim 37, wherein the one or more processors are configured to process the sensor signals to generate a most likely motion state by being further configured to use the information of the one or more operating states of the apparatus to change the probability of one of the tuned states.

39. The apparatus of claim 30, wherein the one or more processors are configured to process the sensor signals to generate a most likely motion state by being further configured to generate, responsive to a first indication that the apparatus is in a charging state and a second indication that the apparatus is in motion, setting a high probability that the apparatus is in an in-transit state.

40. The apparatus of claim 30, wherein the one or more processors are configured to process the sensor signals to generate a most likely motion state by being further configured to generate, responsive to a first indication that the apparatus is connected to a car Bluetooth device or a mobile WiFi access point and a second indication that there is motion, setting a high probability that the device, is in an in-transit state.

41. The apparatus of claim 30, wherein the sensor is an accelerometer.

42. A non-transitory machine-readable medium storing a plurality of machine-readable instructions which, when executed by one or more processors, are adapted to cause the one or more processors to:

receive information on WiFi connectivity of a device;

determine a WiFi connectivity state based on the information on WiFi connectivity, wherein the WiFi connectivity state indicates whether the device is connected to a mobile WiFi access point, connected to a non-mobile WiFi access point, or not connected to a WiFi access point;

process the information on WiFi connectivity of the device to set a probability that the device is in a first motion state based on the WiFi connectivity state;

receive one or more sensor signals from one or more sensor devices, wherein characteristics of the one or more sensor signals reflect motion of the device; and

process the one or more sensor signals aided by the probability that the device is in the first motion state to generate a final motion state for the device.

43. The non-transitory machine-readable medium of claim 42, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to bias the final motion state away from the first motion state based on a low probability that the device is in the first motion state as set by processing of the information on WiFi connectivity of the device.

44. The non-transitory machine-readable medium of claim 43, wherein the processing the one or more sensor signals further comprises eliminating, responsive to a very low probability that the device is in the first motion state, the first motion state from consideration.

45. The non-transitory machine-readable medium of claim 42, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to bias the final motion state toward the first motion state based on a high probability that the device is in the first motion state as set by processing of the information on WiFi connectivity of the device.

46. The non-transitory machine-readable medium of claim 42, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to:

set a low probability that the device is in an in-transit state if the WiFi connectivity state indicates that the device is connected to a non-mobile WiFi access point.

47. The non-transitory machine-readable medium of claim 42, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to:

set a high probability that the device is in an in-transit state if the WiFi connectivity state indicates that the device is connected to a mobile WiFi access point.

48. The non-transitory machine-readable medium of claim 42, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to process information on a plurality of periodic WiFi scans performed by the device if the WiFi connectivity state indicates that the device is not connected to a WiFi access point.

49. The non-transitory machine-readable medium of claim 48, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to:

determining if there is a common access point in the plurality of periodic WiFi scans; and

set a low probability that the device is in an in-transit state if there is a common access point in the plurality of periodic WiFi scans.

50. The non-transitory machine-readable medium of claim 48, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to:

set, responsive to an indication that there is no common access point in the plurality of periodic WiFi scans, a high probability that the device is in an in-transit state.

51. A non-transitory machine-readable medium storing a plurality of machine-readable instructions which, when executed by one or more processors, are adapted to cause the one or more processors to:

receive one or more sensor signals, wherein characteristics of the one or more sensor signals reflect motion of a device;

process the one or more sensor signals to generate one or more tuned signals, wherein the processing is configured to detect one or more tuned states of the device, and wherein each of the tuned signals indicates a probability of the one or more tuned states, at least one of the one or more tuned signals indicating whether the device is stationary or in motion; and

if the one or more tuned signals indicate that the device is in motion, determine a final motion state of the apparatus based on one or more operating states of the device, the one or more operating states including one or more of;

a charging state of the device; and

a network connectivity of the device.

52. The non-transitory machine-readable medium of claim 51, wherein one of the one or more tuned states comprises a stationary state of the device.

53. The non-transitory machine-readable medium of claim 51, wherein one of the one or more tuned states comprises a walk state of the device.

54. The non-transitory machine-readable medium of claim 51, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to use the probability of the tuned state indicated by one of the tuned signals to drive the final motion state toward the tuned state to combine the one or more tuned signals.

55. The non-transitory machine-readable medium of claim 51, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to bypass the processing of the one or more sensor signals to generate a most likely motion state when the probability of the tuned state indicated by one of the tuned signals expresses a high probability of the tuned state.

56. The non-transitory machine-readable medium of claim 55, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to, responsive to the probability of a stationary state indicated by one of the tuned signals for the stationary state exceeding a threshold, bypass the processing of the one or more sensor signals to generate a most likely motion state and set the final motion state as the stationary state.

57. The non-transitory machine-readable medium of claim 55, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to, responsive to the probability of a walk state indicated by one of the tuned signals for the walk state exceeding a threshold, bypass the processing the one or more sensor signals to generate a most likely motion state and set the final motion state as the walk state.

58. The non-transitory machine-readable medium of claim 51, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to process information of the one or more operating states of the device to drive the most likely motion state.

59. The non-transitory machine-readable medium of claim 58, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to use the information of the operating state of the device to change the probability of one of the one or more tuned states to generate the most likely motion state.

60. The non-transitory machine-readable medium of claim 51, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to, responsive to a first indication that the device is in a charging state and a second indication based on one of the tuned signals that there is motion, set a high probability that the device is in an in-transit state.

61. The non-transitory machine-readable medium of claim 51, wherein the plurality of machine-readable instructions are further configured to cause the one or more processors to, responsive to a first indication that the device is connected to a car Bluetooth device or a mobile WiFi access point and a second indication that there is motion, set a high probability that the device is in an in-transit state.

62. The non-transitory machine-readable medium of claim 51, wherein one of the one or more sensor signals is received from an accelerometer.

63. A system, comprising:

means for generating information on WiFi connectivity of the system;

means for determining a WiFi connectivity state based on the information on WiFi connectivity, wherein the WiFi connectivity state indicates whether the device is connected to a mobile WiFi access point, connected to a non-mobile WiFi access point, or not connected to a WiFi access point;

means for processing the information on WiFi connectivity of the system to set a probability that the system is in a first motion state based on the WiFi connectivity state;

means for registering a motion of the system in sensor signals, wherein characteristics of the sensor signals reflect motion of the system; and

means for processing the sensor signals aided by the probability that the system is in the first motion state to generate a final motion state for the system.

64. The system of claim 63, wherein the means for processing the sensor signals further comprises means for biasing the final motion state away from the first motion state based on a low probability that the system is in the first motion state as set by the means for processing the information on WiFi connectivity of the system.

65. The system of claim 63, wherein the means for processing the sensor signals further comprises means for eliminating, responsive to a very low probability that the system is in a first motion state, the first motion state from consideration for the final motion state.

66. The system of claim 63, wherein the means for processing the sensor signals further comprises means for biasing the final motion state toward the first motion state based on a high probability that the system is in the first motion state as set by the means for processing the information on WiFi connectivity of the system.

67. The system of claim 63, wherein the means for processing the information on WiFi connectivity of the system further comprises means for setting a low probability that the system is in an in-transit state if the WiFi connectivity state indicates that the device is connected to a non-mobile WiFi access point.

68. The system of claim 63, wherein the means for processing the information on WiFi connectivity of the system further comprises means for setting a high probability that the system is in an in-transit state if the WiFi connectivity state indicates that the device is connected to a mobile WiFi access point.

69. The system of claim 63, wherein the means for generating the information on WiFi connectivity of the system comprises means for performing a plurality of periodic WiFi scans, and wherein the means for processing the information on WiFi connectivity of the system further comprises means for processing information on the plurality of periodic WiFi scans if the WiFi connectivity state indicates that the device is not connected to a WiFi access point.

70. The system of claim 69, wherein the means for processing the information on WiFi connectivity of the system further comprises:

means for determining if there is a common access point in the plurality of periodic WiFi scans; and

means for setting a low probability that the system is in an in-transit state if there is a common access point in the plurality of periodic WiFi scans.

71. The system of claim 69, wherein the means for processing the information on WiFi connectivity of the system further comprises means for setting, responsive to an indication that there is no common access point in the plurality of periodic WiFi scans, a high probability that the system is in an in-transit state.

72. A system, comprising:

means for registering a motion of the system in sensor signals, wherein characteristics of the sensor signals reflect the motion of the system;

means for processing the sensor signals to generate one or more tuned signals, wherein the means for processing the sensor signals further comprises means for detecting one or more tuned states of the system, and wherein each of the tuned signals indicates a probability of one of the one or more tuned states, at least one of the one or more tuned signals indicating whether the device is stationary or in motion; and

means for determining, if the one or more tuned signals indicate that the device is in motion, a final motion state of the device based on one or more operating states of the device, the one or more operating states including one or more of;

a charging state of the device; and

a network connectivity of the device.

73. The system of claim 72, wherein one of the one or more tuned states comprises a stationary state.

74. The system of claim 72, wherein one of the one or more tuned states comprises a walk state.

75. The system of claim 72, wherein the means for determining comprises means for using the probability of the tuned state indicated by one of the tuned signals to drive the final motion state toward the tuned state.

76. The system of claim 72, wherein the system further comprises means for bypassing processing of the sensor signals to generate a most likely motion state when the probability of the tuned state indicated by one of the tuned signals expresses a high probability of the tuned state.

77. The system of claim 76, wherein when the probability of a stationary state indicated by one of the tuned signals for the stationary state exceeds a threshold, means for processing the sensor signals to generate a most likely motion state is bypassed, and the means for determining is configured to determine the final motion state is the stationary state.

78. The system of claim 76, wherein when the probability of a walk state indicated by one of the tuned signals for the walk state exceeds a threshold, means for processing the sensor signals to generate a most likely motion state is bypassed, and the means for determining is configured to determine the final motion state is the walk state.

79. The system of claim 72, further comprising means for processing information of the one or more operating states of the system to drive the most likely motion state.

80. The system of claim 79, wherein the means for processing the sensor signals to generate a most likely motion state further comprises means for using the information of the one or more operating states of the system to change the probability of one of the one or more tuned states.

81. The system of claim 72, wherein the means for processing the sensor signals to generate a most likely motion state further comprises means for setting, responsive to a first indication that the system is in a charging state and a second indication that the system is in motion, a high probability that the system is in an in-transit state.

82. The system of claim 72, wherein the means for processing the sensor signals to generate a most likely motion state further comprises means for setting, responsive to a first indication that the system is connected to a car Bluetooth device or a mobile WiFi access point and a second indication that the device is in motion, a high probability that the system is in an in-transit state.

83. The system of claim 72, wherein the means for registering a motion of the system is an accelerometer.