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Improving Human Motion Identification Using Motion Dependent Classification

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Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 736))

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Abstract

In this article, we present a new methodology for human motion identification based on motion dependent binary classifiers that afterwards fuse their decisions to identify an Activity of Daily Living (ADL). Temporal and spectral features extracted from the sensor signals (accelerometer and gyroscope) and concatenated to a single feature vector are used to train motion dependent binary classification models. Each individual model is capable to recognize one motion versus all the others. Afterwards the decisions are combined by a fusion function using as weights the sensitivity values derived from the evaluation of each motion dependent classifier on the provided training set. The proposed methodology was evaluated using SVMs for the motion dependent classifiers and is compared against the common multiclass classification approach optimized using either feature selection or subject dependent classification. The classification accuracy of the proposed methodology reaches 99% offering competitive performance comparing to the other approaches.

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Acknowledgements

The research reported in the present paper was partially supported by the FrailSafe Project (H2020- PHC-21-2015-690140) “Sensing and predictive treatment of frailty and associated co-morbidities using advanced personalized models and advanced interventions”, co-funded by the European Commission under the Horizon 2020 research and innovation program.

Author information

Authors and Affiliations

  1. Multidimentional Data Analysis and Knowledge Discovery Laboratory, Department of Computer Engineering and Informatics, University of Patras, Rion-Patras, Greece

    Evangelia Pippa & Vasileios Megalooikonomou

  2. School of Engineering and Technology, University of Hertfordshire, Hatfield, UK

    Iosif Mporas

Authors
  1. Evangelia Pippa
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  2. Iosif Mporas
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  3. Vasileios Megalooikonomou
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Corresponding author

Correspondence to Evangelia Pippa .

Editor information

Editors and Affiliations

  1. Fraunhofer Institute of Optronics, System Technology and Image Exploitation, Lemgo, Germany

    Carsten Röcker

  2. Imperial College London, London, United Kingdom

    John O'Donoghue

  3. RWTH Aachen University, Aachen, Germany

    Martina Ziefle

  4. School of Computing, Dublin City University, Dublin 9, Ireland

    Markus Helfert

  5. Centre for Gerontology, Dublin City University, Cork, Ireland

    William Molloy

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Pippa, E., Mporas, I., Megalooikonomou, V. (2017). Improving Human Motion Identification Using Motion Dependent Classification. In: Röcker, C., O'Donoghue, J., Ziefle, M., Helfert, M., Molloy, W. (eds) Information and Communication Technologies for Ageing Well and e-Health. ICT4AWE 2016. Communications in Computer and Information Science, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-62704-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-62704-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62703-8

  • Online ISBN: 978-3-319-62704-5

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