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Composition of Complex Motion Models from Elementary Human Motions

  • Jörg Moldenhauer
  • Ingo Boesnach
  • Thorsten Stein
  • Andreas Fischer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4069)

Abstract

An appraisal of human motions and particular motion phases is essential for a good interaction between a human and a humanoid robot. We present a new method for the analysis of human motions and the classification of motion phases. The method allows an automatic composition of a motion model for a complex motion from several elementary models. The elementary models can be retrieved from a motion catalogue according to the requirements of a current motion processing task. The method is based on the analysis of the hidden states in a complex HMM and considers the context of all elementary phases in an entire motion sequence. The analysis of motion phases with the new model is computationally more efficient and yields better recognition rates than conventional motion analysis with HMMs and winner-takes-all strategy.

Keywords

Hide Markov Model Recognition Rate Human Motion Humanoid Robot Hide State 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jörg Moldenhauer
    • 1
  • Ingo Boesnach
    • 1
  • Thorsten Stein
    • 2
  • Andreas Fischer
    • 2
  1. 1.Institute for Algorithms and Cognitive SystemsUniversität Karlsruhe (TH)KarlsruheGermany
  2. 2.Institute for Sports and Sports ScienceUniversität Karlsruhe (TH)KarlsruheGermany

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