Objectively Differentiating Movement Patterns between Elite and Novice Athletes

ABSTRACTIntroductionMovement 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 inter-rater reliability and offer the potential to detect new and important features that may not be observable by the human eye. Applying principal components 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.MethodsMotion 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 linear discriminant analysis with cross-validation were used to determine if skill level could be predicted objectively using whole-body motion data.ResultsDepending on the movement, the validated linear discriminate analysis models accurately classified 70.66-82.91% of athletes as either elite or novice.ConclusionWe 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. 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 inter-rater reliability and offer the potential to detect new and important features that may not be observable by the human eye. Applying principal components 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 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 discriminate analysis models accurately classified 70.66-82.91% of athletes as either elite or novice. Conclusion 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. Corresponding Author: Ryan B. Graham, PhD, Assistant Professor, School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, 125 University Private, Ottawa, ON K1N 6N5, E-mail: rgraham@uottawa.ca, Phone: +1 613 562 5800 x 1025, Fax: +1 613 562 5497 We would like to thank our funding sources: the Natural Sciences and Engineering Research Council (NSERC) of Canada, the Queen Elizabeth II Graduate Scholarships in Science and Technology (QEII-GSST), and the University of Ottawa School of Graduate Studies. In addition, we would like to thank the athletes who partook in the study. The results of the present study do not constitute endorsement by ACSM. In addition, the authors declare that there are no conflicts of interest. Although Motus is a for-profit company and the Director of Research at Motus, Brittany Dowling, is an author on the paper, the approach used in this paper is an objective, data-driven approach and is not only applicable to this data set. The results of this study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation Accepted for Publication: 31 January 2018 © 2018 American College of Sports Medicine

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