Input representations and classification strategies for automated human gait analysis.

Journal: Gait & posture
Published Date: Nov 9, 2019

Abstract

BACKGROUND: Quantitative gait analysis produces a vast amount of data, which can be difficult to analyze. Automated gait classification based on machine learning techniques bear the potential to support clinicians in comprehending these complex data. Even though these techniques are already frequently used in the scientific community, there is no clear consensus on how the data need to be preprocessed and arranged to assure optimal classification accuracy outcomes.

Authors

  • Djordje Slijepcevic
    St. Pölten University of Applied Sciences, Institute for Creative Media Technologies, St. Pölten, Austria. Electronic address: djordje.slijepcevic@fhstp.ac.at.
  • Matthias Zeppelzauer
    St. Pölten University of Applied Sciences, Institute for Creative Media Technologies, St. Pölten, Austria.
  • Caterine Schwab
    St. Pölten University of Applied Sciences, Institute of Health Sciences, St. Pölten, Austria.
  • Anna-Maria Raberger
    St. Pölten University of Applied Sciences, Institute of Health Sciences, St. Pölten, Austria.
  • Christian Breiteneder
    TU Wien, Institute of Visual Computing and Human-Centered Technology, Vienna, Austria.
  • Brian Horsak
    St. Pölten University of Applied Sciences, Institute of Health Sciences, St. Pölten, Austria.

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