Tracking sensor integration system and method for recursive estimation of pose of user's body part

1. A tracking sensor integration system comprising:

a plurality of sensors attached to a wearable garment placed on a user's hand, the plurality of sensors configured to obtain sensor data during a plurality of time frames; and

a controller interfaced with the plurality of sensors, the controller configured, for each time frame of the plurality of time frames, to:

update a measurement gain for a current time frame of the plurality of time frames based on a portion of the sensor data collected for the current time frame and a time frame preceding the current time frame and a value of the measurement gain for the time frame preceding the current time frame,

determine prediction for a pose of the user's hand for the current time frame using sensor data collected for the current time frame and a plurality of estimation parameters for the current time frame,

update the estimation parameters for the current time frame, based in part on the updated measurement gain and the prediction for the pose of the user's hand, the estimation parameters representing weighted regressors describing a direct relation between the sensor data and the pose of the user's hand, and

determine an estimated pose for the user's hand for the current time frame, based in part on the updated estimation parameters and the sensor data collected for the current time frame.

2. The system of claim 1 , wherein the controller is further configured to:

update a measurement covariance based in part on the measurement gain and the sensor data collected for the current time frame; and

update the measurement gain for a time frame following the current time frame, based on the updated measurement covariance and sensor data collected by the plurality of sensors during the time frame following the current time frame.

3. The system of claim 1 , wherein the controller is further configured to:

determine a plurality of weights based on a defined accuracy of estimating the pose of the user's hand;

apply the weights to the sensor data collected for the current time frame to obtain weighed sensor data; and

calculate the measurement gain based in part on the weighted sensor data.

4. The system of claim 1 , wherein the controller is further configured to:

determine measurement data related to the pose of the user's hand using the sensor data collected for the current time frame;

calculate a difference between the measurement data and the prediction for the pose of the user's hand; and

update the estimation parameters for the current time frame based on the measurement gain and the difference.

5. The system of claim 1 , wherein the controller is further configured to:

provide information about the estimated pose for the user's hand to a console for updating a visual presentation of the user's hand on an electronic display of a head-mounted display.

6. The system of claim 1 , wherein the controller is further configured to:

replace, in a memory coupled to the controller, information about the estimation parameters for the time frame preceding the current time frame with information about the updated estimation parameters for the current time frame; and

replace, in the memory, information about sensor data collected by the plurality of sensors elements during the time frame preceding the current time frame with information about the sensor data collected for the current time frame.

7. The system of claim 1 , wherein the controller is further configured to:

determine a vector of regressors for the current time frame based on the sensor data collected for the current time frame; and

determine the estimated pose for the user's hand, based on the updated estimation parameters and the vector of regressors.

8. The system of claim 7 , wherein the controller is further configured to:

determine the prediction for the pose of the user's hand for the current time frame, based on the vector of regressors for the current time frame and the estimation parameters for the current time frame.

9. The system of claim 1 , wherein the plurality of sensors are selected from a group consisting of optical sensors, magnetic sensing sensors, radio frequency (RF) based sensors, a position sensor, and an inertial measurement unit (IMU).

10. A tracking sensor integration system, the system configured to:

obtain sensor data during a plurality of time frames by a plurality of sensors attached to a wearable garment placed on a portion of a user's body;

update a measurement gain for a current time frame of the plurality of time frames based on a portion of the sensor data collected for the current time frame and a time frame preceding the current time frame and a value of the measurement gain for the time frame preceding the current time frame;

determine prediction for a pose of the portion of the user's body for the current time frame using sensor data collected for the current time frame and a plurality of estimation parameters for the current time frame;

update the estimation parameters for the current time frame, based in part on the updated measurement gain and the prediction for the pose of the portion of the user's body, the estimation parameters representing weighted regressors describing a direct relation between the sensor data and the pose of the user's hand; and

determine an estimated pose for the portion of the user's body for the current time frame, based in part on the updated estimation parameters and the sensor data collected for the current time frame.

11. The system of claim 10 , wherein the system is further configured to:

update a measurement covariance based in part on the measurement gain and the sensor data collected for the current time frame; and

update the measurement gain for a time frame following the current time frame, based on the updated measurement covariance and sensor data collected by the plurality of sensors during the time frame following the current time frame.

12. The system of claim 10 , wherein the system is further configured to:

determine measurement data related to the pose of the portion of the user's body using the sensor data collected for the current time frame;

calculate a difference between the measurement data and the prediction for the pose of the portion of the user's body; and

update the estimation parameters for the current time frame based on the measurement gain and the difference.

13. The system of claim 10 , wherein the system is further configured to:

replace, in a memory of the system, information about the estimation parameters for the time frame preceding the current time frame with information about the updated estimation parameters for the current time frame; and

replace, in the memory, information about sensor data collected by the plurality of sensors elements during the time frame preceding the current time frame with information about the sensor data collected for the current time frame.

14. The system of claim 10 , wherein the system is further configured to:

determine a vector of regressors for the current time frame based on the sensor data collected for the current time frame; and

determine the estimated pose for the portion of the user's body, based on the updated estimation parameters and the vector of regressors.

15. The system of claim 14 , wherein the system is further configured to:

determine the prediction for the pose of the portion of the user's body for the current time frame, based on the vector of regressors for the current time frame and the estimation parameters for the current time frame.

16. A method comprising:

obtaining sensor data during a plurality of time frames by a plurality of sensors attached to a wearable garment placed on a portion of a user's body;

updating a measurement gain for a current time frame of the plurality of time frames based on a portion of the sensor data collected for the current time frame and a time frame preceding the current time frame and a value of the measurement gain for the time frame preceding the current time frame;

determining prediction for a pose of the portion of the user's body for the current time frame using sensor data collected for the current time frame and a plurality of estimation parameters for the current time frame;

updating the estimation parameters for the current time frame, based in part on the updated measurement gain and the prediction for the pose of the portion of the user's body, the estimation parameters representing weighted regressors describing a direct relation between the sensor data and the pose of the user's hand; and

determining an estimated pose for the portion of the user's body for the current time frame, based in part on the updated estimation parameters and the sensor data collected for the current time frame.

17. The method of claim 16 , further comprising:

determining a plurality of weights based on a defined accuracy of estimating the pose of the portion of the user's body;

applying the weights to the sensor data collected for the current time frame to obtain weighed sensor data; and

calculating the measurement gain based in part on the weighted sensor data.

18. The method of claim 16 , further comprising:

determining measurement data related to the pose of the portion of the user's body using the sensor data collected for the current time frame;

calculating a difference between the measurement data and the prediction for the pose of the portion of the user's body; and

updating the estimation parameters for the current time frame based on the measurement gain and the difference.

19. The method of claim 16 , further comprising:

determining a vector of regressors for the current time frame based on the sensor data collected for the current time frame; and

determining the estimated pose for the portion of the user's body, based on the updated estimation parameters and the vector of regressors.

20. The method of claim 19 , further comprising:

determine the prediction for the pose of the portion of the user's body for the current time frame, based on the vector of regressors for the current time frame and the estimation parameters for the current time frame.