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International Conference on Artificial Neural Networks

ICANN 2017: Artificial Neural Networks and Machine Learning – ICANN 2017 pp 84–91Cite as

Classifying Bio-Inspired Model of Point-Light Human Motion Using Echo State Networks

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10613)

Abstract

We introduce a feature extraction scheme from a biologically inspired model using receptive fields (RFs) to point-light human motion patterns to form an action descriptor. The Echo State Network (ESN) which also has a biological plausibility is chosen for classification. We demonstrate the efficiency and robustness of applying the proposed feature extraction technique with ESN by constraining the test data based on arbitrary untrained viewpoints, in combination with unseen subjects under the following conditions: (i) lower sub-sampling frame rates to simulate data sequence loss, (ii) remove key points to simulate occlusion, and (iii) include untrained movements such as drunkard’s walk.

Keywords

  • Echo state network
  • Motion capture
  • Motion recognition
  • Biological motion perception
  • Bio-inspired model

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Authors and Affiliations

  1. Institute of Cognitive Science, Osnabrück University, Osnabrück, Germany

    Pattreeya Tanisaro, Constantin Lehman, Leon Sütfeld, Gordon Pipa & Gunther Heidemann

Authors
  1. Pattreeya Tanisaro
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  2. Constantin Lehman
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  3. Leon Sütfeld
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  4. Gordon Pipa
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  5. Gunther Heidemann
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Corresponding author

Correspondence to Pattreeya Tanisaro .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Dr. Alessandra Lintas

  2. University of Genoa, Genoa, Italy

    Stefano Rovetta

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Paul F.M.J. Verschure

  4. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

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Tanisaro, P., Lehman, C., Sütfeld, L., Pipa, G., Heidemann, G. (2017). Classifying Bio-Inspired Model of Point-Light Human Motion Using Echo State Networks. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10613. Springer, Cham. https://doi.org/10.1007/978-3-319-68600-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-68600-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68599-1

  • Online ISBN: 978-3-319-68600-4

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