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Predicting athlete ground reaction forces and moments from motion capture

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Abstract

An understanding of athlete ground reaction forces and moments (GRF/Ms) facilitates the biomechanist’s downstream calculation of net joint forces and moments, and associated injury risk. Historically, force platforms used to collect kinetic data are housed within laboratory settings and are not suitable for field-based installation. Given that Newton’s Second Law clearly describes the relationship between a body’s mass, acceleration, and resultant force, is it possible that marker-based motion capture can represent these parameters sufficiently enough to estimate GRF/Ms, and thereby minimize our reliance on surface embedded force platforms? Specifically, can we successfully use partial least squares (PLS) regression to learn the relationship between motion capture and GRF/Ms data? In total, we analyzed 11 PLS methods and achieved average correlation coefficients of 0.9804 for GRFs and 0.9143 for GRMs. Our results demonstrate the feasibility of predicting accurate GRF/Ms from raw motion capture trajectories in real-time, overcoming what has been a significant barrier to non-invasive collection of such data. In applied biomechanics research, this outcome has the potential to revolutionize athlete performance enhancement and injury prevention.

Using data science to model high-fidelity motion and force plate data frees biomechanists from the laboratory

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Acknowledgements

This project was partially supported by the ARC Discovery Grant DP160101458 and an Australian Government Research Training Program Scholarship. We gratefully acknowledge NVIDIA for providing a GPU through its Hardware Grant Program, and Eigenvector Research for the loan licence of PLS_Toolbox. Portions of data included in this study have been funded by NHMRC grant 400937.

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Authors and Affiliations

  1. School of Human Sciences, The University of Western Australia, Perth, Australia

    William R. Johnson, Cyril J. Donnelly & Jacqueline Alderson

  2. School of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia

    Ajmal Mian

  3. Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia

    David Lloyd

  4. School of Allied Health Sciences, Griffith University, Gold Coast, Australia

    David Lloyd

  5. Sports Performance Research Institute New Zealand (SPRINZ), Auckland University of Technology, Auckland, New Zealand

    Jacqueline Alderson

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  1. William R. Johnson
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  5. Jacqueline Alderson
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Correspondence to William R. Johnson.

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Johnson, W.R., Mian, A., Donnelly, C.J. et al. Predicting athlete ground reaction forces and moments from motion capture. Med Biol Eng Comput 56, 1781–1792 (2018). https://doi.org/10.1007/s11517-018-1802-7

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  • DOI: https://doi.org/10.1007/s11517-018-1802-7

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