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Human actions recognition from motion capture recordings using signal resampling and pattern recognition methods

Author

Listed:
  • Tomasz Hachaj

    (Pedagogical University of Krakow)

  • Marek R. Ogiela

    (AGH University of Science and Technology)

  • Katarzyna Koptyra

    (AGH University of Science and Technology)

Abstract

In this paper we will experimentally prove that after recalculating the motion capture (MoCap) data to position-invariant representation it can be directly used by classifier to successfully recognize various actions types. The assumption on classifier is that it is capable to deal with objects that are described by hundreds of numeric values. The second novelty of this paper is application of neural network trained with the parallel stochastic gradient descent, Random Forests and Support Vector Machine with Gaussian radial basis kernel to perform classification task on gym exercises and karate techniques MoCap datasets. We have tested our approach on two datasets using k-fold cross-validation method. Depending of the dataset we have obtained averaged recognition rate from 100 to 97 %. Our results presented in this work give very important hints for developing similar actions recognition systems because proposed features selection and classification setup seems to guarantee high efficiency and effectiveness.

Suggested Citation

  • Tomasz Hachaj & Marek R. Ogiela & Katarzyna Koptyra, 2018. "Human actions recognition from motion capture recordings using signal resampling and pattern recognition methods," Annals of Operations Research, Springer, vol. 265(2), pages 223-239, June.
    • Handle: RePEc:spr:annopr:v:265:y:2018:i:2:d:10.1007_s10479-016-2308-z
    DOI: 10.1007/s10479-016-2308-z
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    1. Inbal Yahav & Galit Shmueli, 2014. "Outcomes matter: estimating pre-transplant survival rates of kidney-transplant patients using simulator-based propensity scores," Annals of Operations Research, Springer, vol. 216(1), pages 101-128, May.
    2. Ya-Ju Fan & Wanpracha Chaovalitwongse, 2010. "Optimizing feature selection to improve medical diagnosis," Annals of Operations Research, Springer, vol. 174(1), pages 169-183, February.
    3. Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
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