This paper presents an evaluation of spatio-temporal data generated by a dynamic stereo vision sensor in a highdimensional space (3D volume and time) for motion analysis and gesture recognition. In contrast to traditional frame-based (synchronous) stereo cameras, dynamic stereo vision sensors asynchronously generates events upon scene dynamics. Motion activities are intrinsically (on-chip) segmented by the sensor, such that activity, gesture recognition and tracking can be intuitively and efficiently performed. In this work, we investigated the applicability of this sensor for gesture recognition. We developed a machine lerning method based on the Hidden Markow Model for training and automated classifications of gestures using the event data generated by the sensor. By training eight different activities (dance figures) with 15 persons we build up a library of 580 recorded activites. An average recognition rate of 97% has been reached.
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