Objectively differentiating movement patterns between elite and novice athletes - Ross, G. B.; Dowling, B.; Troje, N. F.; Fischer, S. L.; Graham, R. B. (Medicine & Science in Sports & Exercise, 2018)
Introduction Movement screens are frequently used to identify abnormal movement patterns that may increase risk of injury or hinder performance. Abnormal patterns are often detected visually based on the observations of a coach or clinician. Quantitative or data-driven methods can increase objectivity, remove issues related to interrater reliability and offer the potential to detect new and important features that may not be observable by the human eye. Applying principal component analysis (PCA) to whole-body motion data may provide an objective data-driven method to identify unique and statistically important movement patterns, an important first step to objectively characterize optimal patterns or identify abnormalities. Therefore, the primary purpose of this study was to determine if PCA could detect meaningful differences in athletes` movement patterns when performing a non-sport-specific movement screen. As a proof of concept, athlete skill level was selected a priori as a factor likely to affect movement performance.