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Multi-sensor Acceleration-Based Action Recognition

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Image Analysis and Recognition (ICIAR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8815))

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Abstract

In this paper, a framework to recognize human actions from acceleration data is proposed. An important step for an accurate recognition is the pre-processing of input data and the following classification by the machine learning algorithm. In this paper, we suggest to combine Dynamic Time Warping (DTW) with Random Forest. The intention of using DTW is to pre-process the data to eliminate outliers and to align the time series. Many applications require more than one inertial sensor for an accurate prediction of actions. In this paper, nine inertial sensors are deployed to ensure an accurate recognition of actions. Further, sensor fusion approaches are introduced and the most promising strategy is shown. The proposed framework is evaluated on a self-recorded dataset consisting of six human actions. Each action was performed three times by 20 subjects. The dataset is publicly available for download.

This work has been partially funded by the ERC within the starting grant Dynamic MinVIP.

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Author information

Authors and Affiliations

  1. Institut für Informationsverarbeitung (TNT), Leibniz Universität Hannover, Hannover, Germany

    Florian Baumann & Bodo Rosenhahn

  2. Institute for Systems Engineering (RTS), Leibniz Universität Hannover, Hannover, Germany

    Irina Schulz

Authors
  1. Florian Baumann
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  2. Irina Schulz
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  3. Bodo Rosenhahn
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Corresponding author

Correspondence to Florian Baumann .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Porto, Porto, Portugal

    Aurélio Campilho

  2. Dept. of Electrical and Computer Eng., University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

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Baumann, F., Schulz, I., Rosenhahn, B. (2014). Multi-sensor Acceleration-Based Action Recognition. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-11755-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11754-6

  • Online ISBN: 978-3-319-11755-3

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