Applying pattern recognition to understand inter-individual variability during the deep squat and hurdle step

Principal component analysis (PCA) [1] was used as a pattern recognition tool to detect principal modes of variability in whole body movement patterns during performance of the deep squat (DS), right hurdle step (RHS) and left hurdle step (LHS). PCA successfully identified features of the DS and hurdle step (HS) that explained the greatest amount of inter-individual variation. Features identified inter-individual variation in squat depth and centre of mass (COM) position, particularly at the midpoint (50%) of each movement.