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Gesture Data Modeling and Classification Based on Critical Points Approximation

  • Michał Cholewa
  • Przemysław Głomb
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)

Abstract

Human-Computer Interaction (HCI) using natural gestures is one of the promising developments in User Interface technology. One of key issues in its design is reliable modeling and classification of gesture data. In this article, we present a method for abstraction of gesture movement information, by reducing it to a sequence of approximated critical points (locations and types). The sequence of such critical points has good feature extraction properties. We present the method, results of classification, and discussion of the properties of the representation based on example gesture dataset recorded with motion capture equpiment.

Keywords

Motion Capture Shape Description Sensor Reading Motion Capture Data Dominant Point 
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 2011

Authors and Affiliations

  • Michał Cholewa
    • 1
  • Przemysław Głomb
    • 1
  1. 1.Institute of Theoretical and Applied Informatics of PASGliwicePoland

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