A Hidden Markov Model-based continuous gesture recognition system for hand motion trajectory

In this paper, we propose an automatic system that recognizes both isolated and continuous gestures for Arabic numbers (0-9) in real-time based on hidden Markov model (HMM). To handle isolated gestures, HMM using ergodic, left-right (LR) and left-right banded (LRB) topologies with different number of states ranging from 3 to 10 is applied. Orientation dynamic features are obtained from spatio-temporal trajectories and then quantized to generate its codewords. The continuous gestures are recognized by our novel idea of zero-codeword detection with static velocity motion. Therefore, the LRB topology in conjunction with forward algorithm presents the best performance and achieves average rate recognition 98.94% and 95.7% for isolated and continuous gestures, respectively.