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Analysing kinematic data from recreational runners using functional data analysis

Author

Listed:
  • Edward Gunning

    (University of Limerick)

  • Steven Golovkine

    (University of Limerick)

  • Andrew J. Simpkin

    (University of Galway)

  • Aoife Burke

    (Dublin City University)

  • Sarah Dillon

    (Dublin City University
    University of Limerick)

  • Shane Gore

    (Dublin City University
    Dublin City University)

  • Kieran Moran

    (Dublin City University
    Dublin City University)

  • Siobhan O’Connor

    (Dublin City University)

  • Enda White

    (Dublin City University)

  • Norma Bargary

    (University of Limerick)

Abstract

We present a multivariate functional mixed effects model for kinematic data from a large number of recreational runners. The runners’ sagittal plane hip and knee angles are modelled jointly as a bivariate function with random effects functions accounting for the dependence among bilateral measurements. The model is fitted by applying multivariate functional principal component analysis (mv-FPCA) and modelling the mv-FPCA scores using scalar linear mixed effects models. Simulation and bootstrap approaches are introduced to construct simultaneous confidence bands for the fixed effects functions, and covariance functions are reconstructed to summarise the variability structure in the data and thoroughly investigate the suitability of the proposed model. In our scientific application, we observe a statistically significant effect of running speed on both joints. We observe strong within-subject correlations, reflecting the highly idiosyncratic nature of running technique. Our approach is applicable to modelling multiple streams of smooth biomechanical data collected in complex experimental designs.

Suggested Citation

  • Edward Gunning & Steven Golovkine & Andrew J. Simpkin & Aoife Burke & Sarah Dillon & Shane Gore & Kieran Moran & Siobhan O’Connor & Enda White & Norma Bargary, 2025. "Analysing kinematic data from recreational runners using functional data analysis," Computational Statistics, Springer, vol. 40(4), pages 1825-1847, April.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-024-01591-1
    DOI: 10.1007/s00180-024-01591-1
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    References listed on IDEAS

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    Cited by:

    1. Pierpaolo D’Urso & Michele Gallo & Paola Zuccolotto, 2025. "Editorial: special issue on sports data science," Computational Statistics, Springer, vol. 40(4), pages 1683-1688, April.

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