The Variance Needed to Accurately Describe Jump Height from Vertical Ground Reaction Force Data

in Journal of Applied Biomechanics

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Chris RichterDublin City University
Sports Surgery Clinic

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Kevin McGuinnessDublin City University

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Noel E. O’ConnorDublin City University

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Kieran MoranDublin City University

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In functional principal component analysis (fPCA) a threshold is chosen to define the number of retained principal components, which corresponds to the amount of preserved information. A variety of thresholds have been used in previous studies and the chosen threshold is often not evaluated. The aim of this study is to identify the optimal threshold that preserves the information needed to describe a jump height accurately utilizing vertical ground reaction force (vGRF) curves. To find an optimal threshold, a neural network was used to predict jump height from vGRF curve measures generated using different fPCA thresholds. The findings indicate that a threshold from 99% to 99.9% (6–11 principal components) is optimal for describing jump height, as these thresholds generated significantly lower jump height prediction errors than other thresholds.

Chris Richter is with Applied Sports Performance Research in the School of Health and Human Performance, CLARITY: Centre for Sensor Web Technologies, and INSIGHT: Centre for Data Analytics at Dublin City University, Dublin, Ireland, as well as Sports Surgery Clinic, Santry Demense, Dublin, Ireland. Kevin McGuinness and Noel E. O’Conner are with CLARITY: Centre for Sensor Web Technologies and INSIGHT: Centre for Data Analytics at Dublin City University, Dublin, Ireland. Kieran Moran is with Applied Sports Performance Research in the School of Health and Human Performance and INSIGHT: Centre for Data Analytics at Dublin City University, Dublin, Ireland. Address author correspondence to Chris Richter at chris.richter@dcu.ie.

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