Chapter

Gesture in Embodied Communication and Human-Computer Interaction

Volume 5934 of the series Lecture Notes in Computer Science pp 73-84

Continuous Realtime Gesture Following and Recognition

  • Frédéric BevilacquaAffiliated withReal Time Musical Interactions Team, IRCAM, CNRS - STMS
  • , Bruno ZamborlinAffiliated withReal Time Musical Interactions Team, IRCAM, CNRS - STMS
  • , Anthony SypniewskiAffiliated withReal Time Musical Interactions Team, IRCAM, CNRS - STMS
  • , Norbert SchnellAffiliated withReal Time Musical Interactions Team, IRCAM, CNRS - STMS
  • , Fabrice GuédyAffiliated withReal Time Musical Interactions Team, IRCAM, CNRS - STMS
  • , Nicolas RasamimananaAffiliated withReal Time Musical Interactions Team, IRCAM, CNRS - STMS

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Abstract

We present a HMM based system for real-time gesture analysis. The system outputs continuously parameters relative to the gesture time progression and its likelihood. These parameters are computed by comparing the performed gesture with stored reference gestures. The method relies on a detailed modeling of multidimensional temporal curves. Compared to standard HMM systems, the learning procedure is simplified using prior knowledge allowing the system to use a single example for each class. Several applications have been developed using this system in the context of music education, music and dance performances and interactive installation. Typically, the estimation of the time progression allows for the synchronization of physical gestures to sound files by time stretching/compressing audio buffers or videos.

Keywords

gesture recognition gesture following Hidden Markov Model music interactive systems