Type

Journal Article

Authors

Kieran Moran
Noel E. O'Connor
Kevin McGuinness
Chris Richter

Subjects

Physiotherapy & Sport

Topics
reaction vertical height functional principal component analysis neural network force variance ground

The variance needed to accurately describe jump height from vertical ground reaction force data. (2014)

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.
Collections Ireland -> Dublin City University -> PubMed

Full list of authors on original publication

Kieran Moran, Noel E. O'Connor, Kevin McGuinness, Chris Richter

Experts in our system

1
Kieran Moran
Dublin City University
Total Publications: 118
 
2
Noel E. O'Connor
Dublin City University
Total Publications: 420
 
3
Kevin McGuinness
Dublin City University
Total Publications: 65
 
4
Chris Richter
Dublin City University
Total Publications: 39