Adaptive Gesture Recognition with Variation Estimation for Interactive Systems

Caramiaux, Baptiste; Montecchio, Nicola; Tanaka, Atau and Bevilacqua, Frédéric. 2014. Adaptive Gesture Recognition with Variation Estimation for Interactive Systems. ACM Transactions on Interactive Intelligent Systems (ACM TiiS), 4(4), [Article]

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Abstract or Description

This paper presents a gesture recognition/adaptation system for Human Computer Interaction applications that goes beyond activity classification and that, complementary to gesture labeling, characterizes the movement execution. We describe a template-based recognition method that simultaneously aligns the input gesture to the templates using a Sequential Montecarlo inference technique. Contrary to standard template- based methods based on dynamic programming, such as Dynamic Time Warping, the algorithm has an adaptation process that tracks gesture variation in real-time. The method continuously updates, during execution of the gesture, the estimated parameters and recognition results which offers key advantages for continuous human-machine interaction. The technique is evaluated in several different ways: recognition and early recognition are evaluated on a 2D onscreen pen gestures; adaptation is assessed on synthetic data; and both early recognition and adaptation is evaluation in a user study involving 3D free space gestures. The method is not only robust to noise and successfully adapts to parameter variation but also performs recognition as well or better than non-adapting offline template-based methods.

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The code is available on Github as ofxGVF. Also please see the dedicated page:

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Departments, Centres and Research Units:

Computing > Embodied AudioVisual Interaction Group (EAVI)


December 2014Published
9 July 2014Accepted

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Date Deposited:

04 Aug 2014 07:34

Last Modified:

29 Apr 2020 16:00

Peer Reviewed:

Yes, this version has been peer-reviewed.


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