Calibration of grab detection

1. A method comprising:

by a computing device, receiving real-time sensor data from N sensors on the computing device, the real-time sensor data corresponding to a transition in a physical state of the computing device caused by a user of the computing device;

by the computing device, applying a linear function to the real-time sensor data from each of the N sensors;

by the computing device, determining a vector based on an N-tuple comprising the derivatives;

by the computing device, comparing the vector with a pre-determined hyperplane with N−1 dimensions; and

by the computing device, determining based on the comparison whether the transition is:

an event corresponding to any of one or more pre-determined imminent uses of the computing device by the user; or

a non-event not corresponding to any of the pre-determined imminent uses of the computing device by the user.

2. The method of claim 1 , further comprising, by the computing device, receiving data defining the pre-determined hyperplane from a computing device of a social-networking system.

3. The method of claim 1 , further comprising:

by the computing device, sending the real-time sensor data to a computing device of a social-networking system; and

by the computing device, receiving updated data re-defining the pre-determined hyperplane based at least in part on the real-time sensor data from the computing device of the social-networking system.

4. The method of claim 1 , wherein the linear function comprises a filtering function, derivative function, convolution of a Heaviside or sigmoid function, or any combination thereof.

5. The method of claim 1 , wherein:

the computing device is a mobile computing device;

the imminent intended use corresponds to physical contact between the user and the mobile computing device; and

the pre-determined function comprises powering on the mobile computing device.

6. The method of claim 1 , wherein one or more of the sensors comprises a touch sensor, gyroscope, accelerometer, optical proximity sensor, ambient light sensor, or any combination thereof.

7. The method of claim 1 , wherein the comparison comprises:

by the computing device, calculating a dot product of the vector and the pre-determined hyperplane; and

by the computing device, determining a position of the vector relative to the pre-determined hyperplane based at least in part on the calculation of the dot product.

8. The method of claim 7 , wherein determining whether the transition is an event comprises, by the computing device, determining the position of the vector is on same side of the pre-determined hyperplane as training data associated with the pre-determined imminent use.

9. One or more computer-readable non-transitory storage media embodying software configured when executed to:

receive real-time sensor data from N sensors on a computing device, the real-time sensor data corresponding to a transition in a physical state of the computing device caused by a user of the computing device;

apply a linear function to the real-time sensor data from each of the N sensors;

determine a vector based on an N-tuple comprising the derivatives;

compare the vector with a pre-determined hyperplane with N−1 dimensions; and

determine based on the comparison whether the transition is:

an event corresponding to any of one or more pre-determined imminent uses of the computing device by the user; or

a non-event not corresponding to any of the pre-determined imminent uses of the computing device by the user.

10. The media of claim 9 , wherein the software is further configured to receive data defining the pre-determined hyperplane from a computing device of a social-networking system.

11. The media of claim 9 , wherein the software is further configured to:

send the real-time sensor data to a computing device of a social-networking system; and

receive updated data re-defining the pre-determined hyperplane based at least in part on the real-time sensor data from the computing device of the social-networking system.

12. The media of claim 9 , wherein the linear function comprises a filtering function, derivative function, convolution of a Heaviside or sigmoid function, or any combination thereof.

13. The media of claim 9 , wherein:

the computing device is a mobile computing device;

the imminent intended use corresponds to physical contact between the user and the mobile computing device; and

the pre-determined function comprises powering on the mobile computing device.

14. The media of claim 9 , wherein one or more of the sensors comprises a touch sensor, gyroscope, accelerometer, optical proximity sensor, ambient light sensor, or any combination thereof.

15. The media of claim 9 , wherein the software is further configured to:

calculate a dot product of the vector and the pre-determined hyperplane; and

determine a position of the vector relative to the pre-determined hyperplane based at least in part on the calculation of the dot product.

16. The media of claim 15 , wherein the software is further configured to determine the position of the vector is on same side of the pre-determined hyperplane as training data associated with the pre-determined imminent use.

17. The device of claim 9 , wherein the software is further configured to:

calculate a dot product of the vector and the pre-determined hyperplane; and

determine a position of the vector relative to the pre-determined hyperplane based at least in part on the calculation of the dot product.

18. The device of claim 17 , wherein the software is further configured to determine the position of the vector is on same side of the pre-determined hyperplane as training data associated with the pre-determined imminent use.

19. A device comprising:

a processor; and

one or more computer-readable non-transitory storage media coupled to the processor and embodying software that is configured when executed to:

receive real-time sensor data from N sensors on the device, the real-time sensor data corresponding to a transition in a physical state of the device caused by a user of the device;

apply a linear function to the real-time sensor data from each of the N sensors;

determine a vector based on an N-tuple comprising the derivatives;

compare the vector with a pre-determined hyperplane with N−1 dimensions; and

determine based on the comparison whether the transition is:

an event corresponding to any of one or more pre-determined imminent uses of the device by the user; or

a non-event not corresponding to any of the pre-determined imminent uses of the device by the user.

20. The device of claim 19 , wherein the software is further configured to receive data defining the pre-determined hyperplane from a computing device of a social-networking system.

21. The device of claim 19 , wherein the software is further configured to:

send the real-time sensor data to a computing device of a social-networking system; and

receive updated data re-defining the pre-determined hyperplane based at least in part on the real-time sensor data from the computing device of the social-networking system.

22. The device of claim 19 , wherein the linear function comprises a filtering function, derivative function, convolution of a Heaviside or sigmoid function, or any combination thereof.

23. The device of claim 19 , wherein:

the device is a mobile computing device;

the imminent intended use corresponds to physical contact between the user and the device; and

the pre-determined function comprises powering on the device.

24. The device of claim 19 , wherein one or more of the sensors comprises a touch sensor, gyroscope, accelerometer, optical proximity sensor, ambient light sensor, or any combination thereof.