Accelerometer Based Gesture Recognition Using Continuous HMMs

This paper presents a gesture recognition system based on continuous hidden Markov models. Gestures here are hand movements which are recorded by a 3D accelerometer embedded in a handheld device. In addition to standard hidden Markov model classifier, the recognition system has a preprocessing step which removes the effect of device orientation from the data. The performance of the recognizer is evaluated in both user dependent and user independent cases. The effects of sample resolution and sampling rate are studied in the user dependent case.

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