Objectively Differentiating Movement Patterns between Elite and Novice Athletes

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. METHODS: Motion capture data from 542 athletes performing seven dynamic screening movements (i.e., bird-dog, drop-jump, T-balance, step-down, L-hop, hop-down, and lunge) were analyzed. A PCA-based pattern recognition technique and a linear discriminant analysis with cross-validation were used to determine if skill level could be predicted objectively using whole-body motion data. RESULTS: Depending on the movement, the validated linear discriminant analysis models accurately classified 70.66% to 82.91% of athletes as either elite or novice. CONCLUSIONS: We have provided proof that an objective data-driven method can detect meaningful movement pattern differences during a movement screening battery based on a binary classifier (i.e., skill level in this case). Improving this method can enhance screening, assessment, and rehabilitation in sport, ergonomics, and medicine.