Principal component analysis (PCA) is a tool able to transform data into a new space in which components (PCs) are uncorellated to each othes. PCs’ magnitudes reflect the amount of variation of the dataset and are normally sorted from the greatest to the smallest. PCA peculiarity is the capability to identify patterns embedded in the data. PCA has been previously used in biomechanics to analyse features associates with knee osteoartitis (Deluzio et al, 2007) and to underline differences between higher and lower skilled race walkers (Donà et al, 2009). The aim of this study was to apply PCA in the analysis of ground reaction force curves in walking and Nordic Walking.
Application of principal component analysis for the assessment of ground reaction forces in walking and nordic walking
PELLEGRINI, Barbara;BORTOLAN, Lorenzo;Zoppirolli, Chiara;FUMENE FERUGLIO, Paolo;SCHENA, Federico
2011-01-01
Abstract
Principal component analysis (PCA) is a tool able to transform data into a new space in which components (PCs) are uncorellated to each othes. PCs’ magnitudes reflect the amount of variation of the dataset and are normally sorted from the greatest to the smallest. PCA peculiarity is the capability to identify patterns embedded in the data. PCA has been previously used in biomechanics to analyse features associates with knee osteoartitis (Deluzio et al, 2007) and to underline differences between higher and lower skilled race walkers (Donà et al, 2009). The aim of this study was to apply PCA in the analysis of ground reaction force curves in walking and Nordic Walking.
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