title: The variance needed to accurately describe jump height from vertical ground reaction force data.
abstract: 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.
authors.affiliation: 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.
authors.forename: Chris
authors.initials: C
authors.lastname: Richter
authors.forename: Kevin
authors.initials: K
authors.lastname: McGuinness
authors.forename: Noel E
authors.initials: NE
authors.lastname: O'Connor
authors.forename: Kieran
authors.initials: K
authors.lastname: Moran
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D003627:Data Interpretation, Statistical; D005528:Foot / Q000502:physiology*; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D005082:Physical Exertion; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013314:Stress, Mechanical; D013647:Task Performance and Analysis