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Feature-Based Gait Pattern Classification for a Robotic Walking Frame

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Advanced Analytics and Learning on Temporal Data (AALTD 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11986))

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Abstract

This paper presents a system for fast detection of gait patterns of walking frame users, where the challenge is to recognize a change in activity before the signal behaves stationary. The system is used as a basis for inferring the user’s intention in order to develop an improved shared-control strategy for an electric-driven walking frame. The data required for gait pattern identification is recorded by a set of low budget infrared distance sensors. We compare different sliding window based feature extraction methods in combination with classical machine learning algorithms in order to realize a fast real-time online gait classification. Moreover, a simple hierarchical feature extraction method is proposed and evaluated on our data-set.

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Acknowledgments

This work was supported by a grant from the German Ministry of Education and Research (BMBF; KMU-innovativ: Medizintechnik, 13GW0173E).

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Authors and Affiliations

  1. Institut für Künstliche Intelligenz, Ravensburg-Weingarten University of Applied Sciences, Weingarten, Germany

    Christopher M. A. Bonenberger, Benjamin Kathan & Wolfgang Ertel

Authors
  1. Christopher M. A. Bonenberger
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  2. Benjamin Kathan
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  3. Wolfgang Ertel
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Corresponding author

Correspondence to Wolfgang Ertel .

Editor information

Editors and Affiliations

  1. Orange Labs, Lannion, France

    Vincent Lemaire

  2. Inria, University of Rennes, Rennes, France

    Simon Malinowski

  3. University of East Anglia, Norwich, UK

    Anthony Bagnall

  4. Orange Labs, Châtillon, France

    Alexis Bondu

  5. Irisa, Agrocampus Ouest, Rennes, France

    Thomas Guyet

  6. University of Rennes 2, Rennes, France

    Romain Tavenard

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Bonenberger, C.M.A., Kathan, B., Ertel, W. (2020). Feature-Based Gait Pattern Classification for a Robotic Walking Frame. In: Lemaire, V., Malinowski, S., Bagnall, A., Bondu, A., Guyet, T., Tavenard, R. (eds) Advanced Analytics and Learning on Temporal Data. AALTD 2019. Lecture Notes in Computer Science(), vol 11986. Springer, Cham. https://doi.org/10.1007/978-3-030-39098-3_8

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  • DOI: https://doi.org/10.1007/978-3-030-39098-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39097-6

  • Online ISBN: 978-3-030-39098-3

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