Gesture Recognition with Hidden Markov Models to Enable Multi-modal Haptic Feedback

Authors:
Vadim Frolov;Barbara Deml;Gunter Hannig
Affiliations:
Fachgebiet Verteilte Messsysteme, Technische Universität München, München, Germany 80399;Human Factors Institute, Universität der Bundeswehr, Neubiberg, Germany 85579;Human Factors Institute, Universität der Bundeswehr, Neubiberg, Germany 85579
Venue:
EuroHaptics '08 Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios
Year:
2008

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Abstract

The ability to recognize and classify human gestures bears a huge potential of improving haptic feedback in virtual environments: when it is possible to predict how a user wants to explore or manipulate his environment both the visual and the auditive feedback may be adapted in order to enhance the immersiveness of haptic displays. Within this work a software approach is suggested that allows for such a real-time classification of gestures in a continuous data stream. A visually based tracking system is used to record the hand movements, while Hidden Markov models are applied to analyze the data. After presenting the methodological background and the software implementation, the outcome of an evaluation study is discussed. It reveals satisfying results of gesture classification and, what is particularly important, the recognition is fast enough to enable multi-modal haptic feedback.