A dynamic gesture interface for virtual environments based on hidden Markov models

A dynamic gesture interface for virtual environments based on hidden Markov models (HMMs) is introduced in this paper. The HMMs are employed to represent the continuous dynamic gestures, and their parameters are learned from the training data collected from the CyberGlove. To avoid the gesture spotting problem, we employed the standard deviation of the angle variation for each finger joint to describe the dynamic characters of the gestures. A prototype which applies 3 different dynamic gestures to control the rotation directions of a 3D cube is implemented to test the effectiveness of the proposed method.