Machine learning prediction of ACL loading during the wide lunge: a multifactorial coupling analysis based on...

In badminton, the wide lunge is a common movement that is highly associated with anterior cruciate ligament (ACL) injury. Existing assessment methods mainly rely on subjective observation, three-dimensional motion capture systems, and surface electromyography signal acquisition, which makes it difficult to rapidly reveal the mechanisms of multi-joint coupling. This study developed and compared six machine learning (ML) algorithms extreme gradient boosting(XGBoost); gradient boosting decision tree (GBDT); random forest (RF); k-nearest neighbours (KNN); kernel ridge regression (KRR); support vector regression (SVR) to predict model-estimated ACL loading during the badminton wide lunge, expressed in multiples of body weight (BW). SHAP was used to rank feature importance and quantify feature contributions.

A total of 237 amateur players were recruited, with kinematic and surface EMG data synchronously collected using a Qualisys motion capture system, AMTI force platforms, and a Delsys surface EMG system during the wide lunge. The sample-size sensitivity and overall predictive performance of the six machine-learning algorithms were evaluated.

XGBoost demonstrated the greatest robustness to sample size variation and achieved the best predictive performance (R ² = 0.94, RMSE = 0.07, MAE = 0.05). Among the nine input features included in the XGBoost model, the SHAP global importance ranking was as follows: knee flexion angle (KFA), hamstrings-to-quadriceps ratio (H/Q), foot progression angle (FPA), internal tibial rotation angle (ITR), hip adduction angle (HAA), ankle dorsiflexion angle (ADF), trunk forward lean angle (TFA), knee valgus angle (KVA), and hip flexion angle (HFA) as the most influential features, with KFA (21.6%), H/Q (16.4%), and FPA (14.4%) contributing most.

XGBoost effectively captured the complex non-linear relationships among biomechanical variables. Combined with SHAP-based explainability analysis, insufficient knee flexion, quadriceps-dominant neuromuscular control, and abnormal foot alignment were identified as a combined model-estimated high ACL loading pattern, suggesting that disrupted coupling between multi-joint coordination and muscle activation may be a key mechanism underlying increased injury risk. These findings may provide a basis for individualised training and injury prevention.