Head tracking correlated motion detection for spatial audio applications

1. A method comprising:

obtaining, using one or more processors of a source device, source device motion data from a source device and headset motion data from a headset worn on a head of a user;

determining, using the one or more processors, correlation measures using the source device motion data and the headset motion data;

updating, using the one or more processors, a motion tracking state based on the determined correlation measures, the updating including transitioning from a single inertial sensor tracking state to a two inertial sensor tracking state, wherein the motion tracking is performed using relative motion data computed from the headset motion data and source device motion data; and

initiating head pose tracking in accordance with the updated motion tracking state.

2. The method of claim 1 , wherein different size windows of motion data are used to compute short term and long term correlation measures.

3. The method of claim 2 , wherein the short term correlation measures are computed based on a short term window of rotation rate data obtained from the source device, a short term window of rotation rate data obtained from the headset, a short term window of relative rotation rate data about a gravity vector, and a variance of the relative rotation rate data.

4. The method of claim 2 , wherein the long term correlation measures are computed based on a long term window of rotation rate data obtained from the source device, a long term window of rotation rate data obtained from the headset, a long term window of relative rotation rate data about a gravity vector, and a variance of the relative rotation rate data.

5. The method of claim 1 , wherein two or more of the correlation measures are logically combined into a single correlation measure indicating whether the source device motion and headset motion are correlated, and the single correlation measure triggers the updating of the motion tracking state from a single inertial sensor tracking state to two inertial sensor tracking state.

6. The method of claim 5 , wherein the single correlation measure includes a confidence measure that indicates a confidence that the user is engaged in a particular activity that results in correlated motion.

7. The method of claim 6 , wherein the particular activity includes at least one of walking or driving in a vehicle.

8. The method of claim 6 , wherein the two or more of the correlation measures include a mean relative rotation rate about a gravity vector, a determination that a mean short term rotation rate of the source device is less than a mean short term rotation rate of the headset and the confidence measure.

9. The method of claim 1 , wherein the motion tracking state is updated from a two inertial sensor tracking state to a single inertial sensor tracking state based on whether the source device is rotating faster than the headset and that the source device rotation is inconsistent.

10. A system comprising:

one or more processors;

memory storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations:

obtaining, using one or more processors of a source device, source device motion data from a source device and headset motion data from a headset worn on a head of a user;

determining, using the one or more processors, correlation measures using the source device motion data and the headset motion data;

updating, using the one or more processors, a motion tracking state based on the determined correlation measures, the updating including transitioning from a single inertial sensor tracking state to a two inertial sensor tracking state, wherein the motion tracking is performed using relative motion data computed from the headset motion data and source device motion data; and

initiating head pose tracking in accordance with the updated motion tracking state.

11. The system of claim 10 , wherein different size windows of motion data are used to compute short term and long term correlation measures.

12. The system of claim 11 , wherein the short term correlation measures are computed based on a short term window of rotation rate data obtained from the source device, a short term window of rotation rate data obtained from the headset, a short term window of relative rotation rate data about a gravity vector, and a variance of the relative rotation rate data.

13. The system of claim 11 , wherein the long term correlation measures are computed based on a long term window of rotation rate data obtained from the source device, a long term window of rotation rate data obtained from the headset, a long term window of relative rotation rate data about a gravity vector, and a variance of the relative rotation rate data.

14. The system of claim 10 , wherein two or more of the correlation measures are logically combined into a single correlation measure indicating whether the source device motion and headset motion are correlated, and the single correlation measure triggers the updating of the motion tracking state from a single inertial sensor tracking state to two inertial sensor tracking state.

15. The system of claim 14 , wherein the single correlation measure includes a confidence measure that indicates a confidence that the user is engaged in a particular activity that results in correlated motion.

16. The system of claim 15 , wherein the particular activity includes at least one of walking or driving in a vehicle.

17. The system of claim 15 , wherein the two or more of the correlation measures include a mean relative rotation rate about a gravity vector, a determination that a mean short term rotation rate of the source device is less than a mean short term rotation rate of the headset and the confidence measure.

18. The system of claim 10 , wherein the motion tracking state is updated from a two inertial sensor tracking state to a single inertial sensor tracking state based on whether the source device is rotating faster than the headset and that the source device rotation is inconsistent.