School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario, CANADA.
Motus Global, Rockville Centre, New York, NY.
Med Sci Sports Exerc. 2018 Jul;50(7):1457-1464. doi: 10.1249/MSS.0000000000001571.
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.
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.
Depending on the movement, the validated linear discriminant analysis models accurately classified 70.66% to 82.91% of athletes as either elite or novice.
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.
运动筛查常用于识别可能增加受伤风险或妨碍表现的异常运动模式。异常模式通常是根据教练或临床医生的观察进行视觉上的检测。定量或数据驱动的方法可以提高客观性，消除与评分者间可靠性相关的问题，并有可能检测到人类眼睛可能无法观察到的新的重要特征。对全身运动数据应用主成分分析（PCA）可能提供一种客观的数据驱动方法来识别独特且具有统计学意义的运动模式，这是客观描述最佳模式或识别异常的重要第一步。因此，本研究的主要目的是确定 PCA 是否可以检测到运动员在进行非特定于运动的运动筛查时运动模式的有意义差异。作为概念验证，运动员的技能水平被预先选择为可能影响运动表现的因素。
对 542 名运动员进行了 7 项动态筛查运动（即鸟狗式、跳落式、T 平衡式、下台阶式、L 跳式、跳下式和弓步式）的运动捕捉数据进行了分析。使用基于 PCA 的模式识别技术和具有交叉验证的线性判别分析来确定是否可以使用全身运动数据客观地预测技能水平。
根据运动的不同，经过验证的线性判别分析模型准确地将 70.66%至 82.91%的运动员分类为精英或新手。
我们已经证明，基于二进制分类器（在这种情况下为技能水平），客观的数据驱动方法可以检测到运动筛查电池中的有意义的运动模式差异。改进这种方法可以增强运动、工效学和医学中的筛查、评估和康复。
