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.
journal_name
J Appl Biomechjournal_title
Journal of applied biomechanicsauthors
Richter C,McGuinness K,O'Connor NE,Moran Kdoi
10.1123/jab.2013-0313subject
Has Abstractpub_date
2014-12-01 00:00:00pages
732-6issue
6eissn
1065-8483issn
1543-2688pii
2013-0313journal_volume
30pub_type
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