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Evaluating 3D Hand Motion with a Softkinetic Camera

Gesture and motion evaluation provide an interface for a variety of human-computer interaction (HCI)applications. In particular, using human hand motions as a natural interface tool has motivated an active research area to conduct studies on modeling, analyzing and recognizing various hand motions. Recently, human-computer interaction has been a focus of research in vision-based gesture recognition. In this work, we propose a 3D hand model evaluation method that can recognize soft and elaborate representations of hand motions. The camera views landmarked points on the tips and joints of the fingers in the front plane and estimates the depth of these points using a Soft Kinetic camera [1], an Hidden Markov Model (HMM) is used to evaluate the hand motions. Experimentally, in an effort to evaluate the formation of hand gestures similar to those used in rehabilitation sessions, we studied three evolving motions. Given natural hand features and an uncontrolled environment, we were able to classify and differentiate any unnatural slowness of such motions.