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Prediction of active peak force using a multilayer perceptron

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Abstract

Both kinematic parameters and ground reaction forces (GRFs) are necessary for understanding the biomechanics of running. Kinematic information of a runner is typically measured by a motion capture system whereas GRF during the support phase of running is measured by force platforms. To analyze both kinematics and kinetics of a runner over several subsequent contacts, an instrumented treadmill or alternatively several force platforms installed over a regulated space are available options, but they are highly immovable, expensive, and sometimes even impractical options. Naturally, it would be highly useful to predict GRFs using a motion capture system only and this way reduce costs and complexity of the analysis. In this study, the machine learning model for vertical GRF magnitude prediction based on running motion information of 128 healthy adults is proposed. The predicted outputs of a multilayer perceptron model were compared with the actual force platform measurements. The results were evaluated with Pearson’s correlation coefficient through a tenfold cross validation. The mean standard error of the estimate was 0.107 body weights showing that our method is sufficiently accurate to identify abnormalities in running technique among recreational runners.

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Acknowledgements

This study was supported by the Finnish Funding Agency for Technology and Innovation (Tekes) and the Academy of Finland.

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Authors and Affiliations

  1. Agora Center, University of Jyväskylä, P.O.Box 35, 40014, Jyväskylä, Finland

    Marko Niemelä, Juha-Pekka Kulmala, Jukka Kosonen & Sami Äyrämö

  2. Niilo Mäki Institute, P.O.Box 35, 40014, Jyväskylä, Finland

    Marko Niemelä

  3. Faculty of Information Technology, University of Jyväskylä, P.O.Box 35, 40014, Jyväskylä, Finland

    Marko Niemelä, Jukka-Pekka Kauppi & Sami Äyrämö

  4. Motion Analysis Laboratory, Children’s Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland

    Juha-Pekka Kulmala

  5. Department of Computer Science and HIIT, University of Helsinki, Helsinki, Finland

    Jukka-Pekka Kauppi

  6. Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland

    Jukka Kosonen

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  1. Marko Niemelä
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  2. Juha-Pekka Kulmala
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  3. Jukka-Pekka Kauppi
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  4. Jukka Kosonen
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  5. Sami Äyrämö
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Correspondence to Marko Niemelä.

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Niemelä, M., Kulmala, JP., Kauppi, JP. et al. Prediction of active peak force using a multilayer perceptron. Sports Eng 20, 213–219 (2017). https://doi.org/10.1007/s12283-017-0236-z

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  • DOI: https://doi.org/10.1007/s12283-017-0236-z

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