Abstract
This paper presents a method for developing a gesture-based system using a multidimensional hidden Markov model (HMM). Instead of using geometric features, gestures are converted into sequential symbols. HMMs are employed to represent gestures and their parameters are learned from training data. Based on the most likely performance criterion, gestures can be recognized by evaluating trained HMMs. We have developed a prototype to demonstrate feasibility of proposed method. The system achieved 99.78% accuracy for a 9 gesture isolated recognition task. Encouraging results were also obtained from experiments of continuous gesture recognition. The proposed method is applicable to any multidimensional signal representation gesture, and will be a valuable tool in telerobotics and human computer interfacing. >
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