Analysis of 3D Hand Trajectory Gestures Using Stroke-Based Composite Hidden Markov Models

We present a glove-based hand gesture recognition system using hidden Markov models (HMMs) for recognizing the unconstrained 3D trajectory gestures of operators in a remote work environment. A Polhemus sensor attached to a PinchGlove is employed to obtain a sequence of 3D positions of a hand trajectory. The direct use of 3D data provides more naturalness in generating gestures, thereby avoiding some of the constraints usually imposed to prevent performance degradation when trajectory data are projected into a specific 2D plane. We use two kinds of HMMs according to the basic units to be modeled: gesture-based HMM and stroke-based HMM. The decomposition of gestures into more primitive strokes is quite attractive, since reversely concatenating stroke-based HMMs makes it possible to construct a new set of gesture-based HMMs. Any deterioration in performance and reliability arising from decomposition can be remedied by a fine-tuned relearning process for such composite HMMs. We also propose an efficient method of estimating a variable threshold of reliability for an HMM, which is found to be useful in rejecting unreliable patterns. In recognition experiments on 16 types of gestures defined for remote work, the fine-tuned composite HMM achieves the best performance of 96.88% recognition rate and also the highest reliability.