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Recognizing Unseen Gym Activities from Streaming Data - Accelerometer Vs. Electromyogram

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Book cover Distributed Computing and Artificial Intelligence, 13th International Conference

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 474))

Abstract

Wearable sensors based activity recognition is a research area where mostly inertial measurement unit based information is used to recognize human activities. Commonly the approaches are based on accelerometer data while in this study the potential of electromyogram signals in activity recognition is studied. The actual research problem tackled is one of the major drawbacks in activity recognition, namely to add completely new activities in real life to the recognition models. In this study, it was shown that in gym settings electromyogram signals clearly outperforms the accelerometer data in recognition of completely new sets of gym movements from streaming data even though the sensors would not be positioned directly to the muscles trained.

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Author information

Authors and Affiliations

  1. Biomimetics and Intelligent Systems Group, University of Oulu, PO Box 4500, 90014, Oulu, Finland

    Heli Koskimäki & Pekka Siirtola

Authors
  1. Heli Koskimäki
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  2. Pekka Siirtola
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Corresponding author

Correspondence to Heli Koskimäki .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, Osaka Institute of Technology Faculty of Engineering, Osaka, Osaka, Japan

    Sigeru Omatu

  2. Faculty of Computer Science & Informatio, Universiti Teknologi Malaysia (UTM) Faculty of Computer Science & Informatio, Baharu, Malaysia

    Ali Semalat

  3. Department of Electronics and Compu, Koszalin University of Technology Department of Electronics and Compu, Koszalin, Poland

    Grzegorz Bocewicz

  4. Faculty of Electrical Engineering and Cs, Kielce University of Technology Faculty of Electrical Engineering and Cs, Kielce, Poland

    Paweł Sitek

  5. Faculty of Engineering and Science, Aalborg University Faculty of Engineering and Science, Aalborg, Denmark

    Izabela E. Nielsen

  6. ETS Ingeniería Informática, University of Sevilla ETS Ingeniería Informática, Sevilla, Spain

    Julián A. García García

  7. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid Departamento de Inteligencia Artificial, Madrid, Spain

    Javier Bajo

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Koskimäki, H., Siirtola, P. (2016). Recognizing Unseen Gym Activities from Streaming Data - Accelerometer Vs. Electromyogram. In: Omatu, S., et al. Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-319-40162-1_21

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  • DOI: https://doi.org/10.1007/978-3-319-40162-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40161-4

  • Online ISBN: 978-3-319-40162-1

  • eBook Packages: EngineeringEngineering (R0)

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