Wearable device system with driver mode steering detection

• US 9,931,062 B2
• Filed: 06/23/2015
• Issued: 04/03/2018
• Est. Priority Date: 06/23/2015
• Status: Active Grant

• Current Assignee: Motorola Mobility LLC (Lenovo Group Ltd.)
• Original Assignee: Motorola Mobility LLC (Lenovo Group Ltd.)
• Inventors: Cavallaro, Alberto R, Crosbie, Paul B
• Primary Examiner(s): PERT, EVAN T
• Application Number:
  US14/746,992
• Publication Number:
  US 20160374614A1
• Time in Patent Office: 1,015 Days
• CPC Class Codes: A61B 5/1122 of movement trajectories
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: G07C 5/02 Registering or indicating driving, working, idle, or waiting time only apparatus forming part of taximeters G07B13/00
• CPC Class Codes: H04M 1/724095 Worn on the wrist, hand or arm
• CPC Class Codes: H04M 1/72412 using two-way short-range wireless interfaces

Abstract

A system and method of operation involves collecting acceleration and gyroscope data from a first sensor positioned in a wearable device on a user'"'"'s wrist and a second sensor located in a mobile device. The mobile device determines a trajectory for the wearable device by filtering the first sensor data using the second sensor data, and determines a probability of the user holding an automobile steering wheel using the trajectory. The method may also include determining a probability of the user holding an automobile steering wheel of a specific automobile selected from an automobile list. The disclosed system includes the wearable device and the mobile device.

Claims

1. A method comprising:

collecting, by a mobile device, acceleration and gyroscope data from a first sensor positioned in a wearable device on a user'"'"'s wrist and a second sensor located in the mobile device;

determining a trajectory by filtering the first sensor data using the second sensor data; and

determining a probability of the user holding an automobile steering wheel using the trajectory.

2. The method of claim 1, further comprising:

determining a probability of the user holding an automobile steering wheel of a specific automobile using the trajectory, the specific automobile selected from an automobile list.

3. The method of claim 1, wherein determining the probability of the user holding an automobile steering wheel using the trajectory, comprises:

counting the number of occurrences per number of measurements of the acceleration and gyroscope data, in which radius of curvature, and angular speed orthogonal to the radio of curvature, are within specified upper and lower limits; and

assigning the probability as a sum of addends comprising the weighted occurrences.

4. The method of claim 3, further comprising:

assigning the probability as a sum of addends comprising the weighted occurrences, wherein one of the addends is a steering wheel tilt angle.

5. The method of claim 4, further comprising:

assigning weighting factors to radius of curvature addend, angular speed orthogonal to the radius of curvature, and steering wheel tilt angel where the sum of the weighting factors is equal to one.

6. The method of claim 1, wherein determining the trajectory comprises:

determining an acceleration vector and a rotation rate vector for the wearable device using the first sensor acceleration and gyroscope data; and

correcting the acceleration vector and the rotation rate vector for the wearable device using the second sensor acceleration and gyroscope data.

7. The method of claim 6, further comprising:

subtracting gravitational effects from the acceleration vector and the rotation rate vector for the wearable device.

8. The method of claim 6, further comprising:

calculating a radius and an angular speed using the acceleration vector and the rotation rate vector.

9. The method of claim 6, further comprising:

determining a steering wheel tilt angle using the first sensor acceleration and gyroscope data.

10. The method of claim 1, wherein determining the trajectory comprises:

establishing a time series equation with an acceleration vector and a rotation rate vector for the wearable device containing the first sensor and for the mobile device;

inputting the acceleration and gyroscope data from the first sensor and from the second sensor into the time series equation;

calculating a gravity unit vector for the wearable device and for the mobile device using the time series equation; and

calculating a relative acceleration vector and a relative rotation rate vector for the wearable device and for the mobile device.

11. A mobile device comprising:

an accelerometer;

peer-to-peer baseband hardware, operatively coupled to at least one antenna;

a processor, operatively coupled to the accelerometer and to the peer-to-peer baseband hardware, and operative to;

collect wearable device acceleration and gyroscope data as first sensor data from a first sensor positioned in a wearable device using a wireless interface implemented using the peer-to-peer baseband hardware, and mobile device acceleration data as second sensor data from the accelerometer;

determine a trajectory by filtering the first sensor data using the second sensor data; and

determine a probability of the wearable device user holding an automobile steering wheel using the trajectory.

12. The mobile device of claim 11, further comprising:

non-transitory, non-volatile memory, operatively coupled to the processor and having an automobile list stored therein; and

wherein the processor is further operative to;

determine a probability of the wearable device user holding an automobile steering wheel of a specific automobile using the trajectory, the specific automobile selected from the automobile list.

13. The mobile device of claim 11, wherein the processor is further operative to determine the probability of the user holding an automobile steering wheel using the trajectory, by:

counting the number of occurrences per number of measurements of the acceleration and gyroscope data, in which radius of curvature, and angular speed orthogonal to the radio of curvature, are within specified upper and lower limits; and

assign the probability as a sum of addends comprising the weighted occurrences.

14. The mobile device of claim 13, wherein the processor is further operative to:

assign the probability as a sum of addends comprising the weighted occurrences, wherein one of the addends is a steering wheel tilt angle.

15. The mobile device of claim 14, wherein the processor is further operative to:

assign weighting factors to radius of curvature addend, angular speed orthogonal to the radius of curvature, and steering wheel tilt angel where the sum of the weighting factors is equal to one.

16. The mobile device of claim 11, wherein the processor is further operative to determine the trajectory by:

determining an acceleration vector and a rotation rate vector for the wearable device using the first sensor acceleration and gyroscope data; and

correcting the acceleration vector and the rotation rate vector for the wearable device using the second sensor acceleration and gyroscope data.

17. The mobile device of claim 16, wherein the processor is further operative to:

subtract gravitational effects from the acceleration vector and the rotation rate vector for the wearable device.

18. The mobile device of claim 16, wherein the processor is further operative to:

calculate a radius and an angular speed using the acceleration vector and the rotation rate vector.

19. The mobile device of claim 16, wherein the processor is further operative to:

determine a steering wheel tilt angle using the first sensor acceleration and gyroscope data.

20. The mobile device of claim 11, wherein the processor is further operative to determine the trajectory by:

establishing a time series equation with an acceleration vector and a rotation rate vector for the wearable device containing the first sensor and for the mobile device;

inputting the acceleration and gyroscope data from the first sensor and from the second sensor into the time series equation;

calculating a gravity unit vector for the wearable device and for the mobile device using the time series equation; and

calculating a relative acceleration vector and a relative rotation rate vector for the wearable device and for the mobile device.