Analysing kinematic data from recreational runners using functional data analysis

Edward Gunning (), Steven Golovkine, Andrew J. Simpkin, Aoife Burke, Sarah Dillon, Shane Gore, Kieran Moran, Siobhan O’Connor, Enda White and Norma Bargary
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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
Shane Gore: Dublin City University
Kieran Moran: Dublin City University
Siobhan O’Connor: Dublin City University
Enda White: Dublin City University
Norma Bargary: University of Limerick

Computational Statistics, 2025, vol. 40, issue 4, No 8, 1825-1847

Abstract: 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.

Keywords: Biomechanics; Functional data analysis; Mixed-effects model; Multivariate functional data (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01591-1

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