足球运动员90°变向的生物力学表型：无监督机器学习在前交叉韧带损伤预防中的应用
简介
该研究利用无监督机器学习算法，分析了1002名健康足球运动员（平均年龄16.3岁）在90°变向任务中的二维运动学、地面反作用力及评分系统数据（共6008次试次），旨在识别不同的生物力学表型。结果显示，共识别出4个具有显著差异的聚类（轮廓系数=0.43）：聚类0为“低地面反作用力、运动控制差”；聚类1为“低地面反作用力、运动控制可接受”；聚类2为“高地面反作用…
英文摘要
PURPOSE: Neuromuscular and biomechanical deficits contribute to anterior cruciate ligament (ACL) injury risk in football. Artificial intelligence (AI) could be adopted to analyse large datasets of complex multidirectional movements, aiding to unravel hidden biomechanical profiles. This study aimed to identify clusters of features describing the 90° change of direction (COD) task in football players-that is, biomechanical phenotypes-through unsupervised machine learning algorithms. METHODS: One thousand and two healthy football players (mean age: 16.3 years) from the 'CutTheACL' project performed a series of 90° COD tasks. Two-dimensional (2D) kinematics and ground reaction forces (GRF) were extracted from three high-speed cameras and one embedded force platform (VICON, AMTI). Unsupervised agglomerative clustering was performed to inspect the full dataset including kinematics, kinetics and 2D scoring system variables for each of the 6008 trials. The Silhouette coefficient was used to inspect the goodness of cluster differentiation. The Kruskal-Wallis and χ2 tests were performed to identify statistical differences among the clusters (p < 0.05). RESULTS: Four clusters with distinct biomechanical traits (phenotype) emerged (silhouette = 0.43). The clusters' interpretation was simplified according to GRFs and ACL-injury risk related 2D kinematics: Cluster 0 'low forces, poor movement control'; Cluster 1 'low forces, acceptable movement control'; Cluster 2 'high forces, poor movement control'; Cluster 3 'high forces, acceptable movement control'. Statistically significant differences emerged among all clusters (p < 0.001). CONCLUSION: Four biomechanical COD phenotypes with distinct neuromuscular control and force profiles were identified through unsupervised clustering. Such an approach might offer a foundation to inspect young football players' motion and target primary ACL injury prevention. LEVEL OF EVIDENCE: Level IV, cohort study.