Identifying a 3-D motion on 2-D planes

1. A computer-implemented method for recognizing a specific gesture based on a 3-D motion of a portable device, the computer-implemented method comprising:

obtaining linear acceleration motion data and device orientation data of the portable device in reference to gravity at a plurality of times at fixed interval, the linear acceleration motion data including a linear acceleration vector in reference to the gravity;

wherein the linear acceleration motion data and the device orientation data in reference to the gravity are obtained by one or more accelerometers associated with the portable device;

obtaining relative angular rotation data of the portable device at the plurality of times, the angular rotation data including an angular velocity vector;

wherein the relative angular rotation data is obtained by one or more gyroscopes, or multiple depth cameras associated with the portable device;

defining a plurality of 2-D local planes at each of the plurality of times according to the linear acceleration vector, and the angular velocity vector obtained at each of the respective plurality of times;

analyzing 2-D kinematic data associated with the motion of the portable device on each of the plurality of defined 2-D local planes, the 2-D kinematic data obtained via the one or more accelerometers and the one or more gyroscopes or multiple depth cameras;

obtaining non-kinematic data of the portable device, the non-kinematic data including audio data from an audio input device associated with the portable device;

wherein the audio data is indicative of a predefined gesture;

identifying the 3-D motion by comparing the 2-D kinematic data associated with the motion of the portable device in one or more of the plurality of 2-D local planes;

identifying the specific gesture by analyzing the identified 3-D motion and the non-kinematic data of the portable device; and

performing or initiating a predefined action indicated by the identified specific gesture.

2. The computer-implemented method as recited in claim 1 , wherein the one or more accelerometers include a 3-axis accelerometer.

3. The computer-implemented method as recited in claim 1 , wherein the relative angular rotation data includes relative degree of rotation data on roll axis, pitch axis, and yaw axis.

4. The computer-implemented method as recited in claim 3 , wherein the one or more gyroscopes include a 3-axis gyroscope.

5. The computer-implemented method as recited in claim 3 , wherein the portable device further includes one or more magnetometer, the one or more magnetometer configured to obtain absolute yaw rotation of the portable device to a fixed direction.

6. The computer-implemented method as recited in claim 1 , further comprising: generating a 3-D motion pattern of an operator of the portable device according to multiple distinct inputs of 3-D motions from the operator.

7. The computer-implemented method as recited in claim 6 , wherein the 3-D motion pattern is dynamically constructed or expanded by analyzing the operator's 3-D motion pattern over time using predefined machine learning algorithms.

8. The computer-implemented method as recited in claim 7 , further comprising: storing the operator's 3-D motion pattern on the portable device, other computing devices of the operator, and/or a cloud computing service.

9. The computer-implemented method as recited in claim 1 , further comprising: providing an operator of the portable device a set of predetermined functions of 3-D motion recognition sensitivity according to the operator's physical characteristics.

10. The computer-implemented method as recited in claim 9 , wherein the portable device includes the audio input device.

11. The computer-implemented method as recited in claim 1 , wherein the portable device is a wireless controller, a personal digital assistant (PDA), a smart phone, a tablet, a Window/Mac laptop, a Linux machine, a netbook, a cloudbook, or a media player.

12. A system for recognizing a specific gesture based on a 3-D motion, the system comprising:

a portable device, the portable device including:

one or more processors;

one or more accelerometers;

one or more gyroscopes;

one or more audio input devices; and

one or more memory units with instructions stored thereon, which when executed by the one or more processors cause the system to:

obtain linear acceleration motion data and device orientation data of the portable device in reference to gravity at a plurality of times at fixed interval, the linear acceleration motion data including a linear acceleration vector in reference to the gravity;

wherein the linear acceleration motion data and the device orientation data in reference to the gravity are obtained by the one or more accelerometers;

obtain relative angular rotation data of the portable device, the angular rotation data including an angular velocity vector;

wherein the relative angular rotation data is obtained by the one or more gyroscopes;

define a plurality of 2-D local planes at each of the plurality of times according to the linear acceleration vector and the angular velocity vector;

analyze 2-D kinematic data associated with the motion of the portable device on each of the plurality of defined 2-D local planes, the 2-D kinematic data obtained via the one or more accelerometers and the one or more gyroscopes;

obtain non-kinematic data of the portable device, the non-kinematic data including audio data from the one or more audio input device associated with the portable device;

wherein, the audio input data is indicative of a predefined gesture;

identify the 3-D motion by comparing the 2-D kinematic data associated with the motion of the portable device in one or more of the plurality of 2-D local planes;

identify the specific gesture by analyzing the identified 3-D motion and the kinematic data of the portable device; and

perform or initiate a predefined action indicated by the identified specific gesture.

13. The system as recited in claim 12 , wherein the one or more accelerometers include a 3-axis accelerometer.

14. The system as recited in claim 12 , wherein the relative angular rotation data includes relative degree of rotation data on roll axis, pitch axis, and yaw axis.

15. The system as recited in claim 14 , wherein the one or more gyroscopes include a 3-axis gyroscope.

16. The system as recited in claim 14 , wherein the portable device further includes one or more magnetometer, the one or more magnetometer configured to obtain absolute yaw rotation of the portable device to a fixed direction.

17. The system as recited in claim 12 , wherein the one or more memory units have further instructions stored thereon, which when executed by the one or more processors cause the system to further: generate a 3-D motion pattern of an operator of the portable device according to multiple distinct inputs of 3-D motions from the operator.

18. The system as recited in claim 17 , wherein the 3-D motion pattern is dynamically constructed or expanded by analyzing the operator's 3-D motion pattern over time using predefined machine learning algorithms.

19. The system as recited in claim 18 , wherein the one or more memory units have further instructions stored thereon, which when executed by the one or more processors cause the system to further: store the operator's 3-D motion pattern on the portable device, other computing devices of the operator, and/or a cloud computing service.

20. The system as recited in claim 12 , wherein the one or more memory units have further instructions stored thereon, which when executed by the one or more processors cause the system to further: provide an operator of the portable device a set of predetermined functions of 3-D motion recognition sensitivity according to the operator's physical characteristics.

21. The system for recognizing a specific gesture based on a 3-D motion as recited in claim 12 , wherein the portable device is a wireless controller, a personal digital assistant (PDA), a smart phone, a tablet, or a laptop.