1. A computer-implemented method, comprising: receiving, by a processor included in a mobile device, accelerometer values for a period of time during which the mobile device completes a gesture; partitioning the accelerometer values into a plurality of distinct segments; detecting a movement of the mobile device based on detecting a set of acceleration values of the mobile device; identifying that the mobile device completed the gesture based on applying the movement of the mobile device to a first Bayesian network and a second Bayesian network, wherein: the first Bayesian network includes a first plurality of states, wherein each respective state in the first plurality of states corresponds to a respective one of the plurality of distinct segments, and wherein the first Bayesian network corresponds to the gesture being performed at a first speed, and the second Bayesian network includes a second plurality of states, wherein each respective state in the second plurality of states corresponds to a respective one of the plurality of distinct segments, and wherein the second Bayesian network corresponds to the gesture being performed at a second speed that is slower than the first speed; and taking an action in response to identifying that the mobile device completed the gesture.
1. A computer-implemented method, comprising: receiving, by a processor included in a mobile device, accelerometer values for a period of time during which the mobile device completes a gesture; partitioning the accelerometer values into a plurality of distinct segments; detecting a movement of the mobile device based on detecting a set of acceleration values of the mobile device; identifying that the mobile device completed the gesture based on applying the movement of the mobile device to a first Bayesian network and a second Bayesian network, wherein: the first Bayesian network includes a first plurality of states, wherein each respective state in the first plurality of states corresponds to a respective one of the plurality of distinct segments, and wherein the first Bayesian network corresponds to the gesture being performed at a first speed, and the second Bayesian network includes a second plurality of states, wherein each respective state in the second plurality of states corresponds to a respective one of the plurality of distinct segments, and wherein the second Bayesian network corresponds to the gesture being performed at a second speed that is slower than the first speed; and taking an action in response to identifying that the mobile device completed the gesture. 13. The computer-implemented method of claim 1 , wherein detecting the movement of the mobile device is further based on camera data corresponding to motion of lips of a user of the mobile device.