Smartphone-Based Interpretable Machine Learning for Classifying Single-Leg Squat Performance Using Trunk, Pelvic, and Knee Kinematics: Cross-Sectional Study

This study presents an interpretable machine learning framework for classifying SLS performance into 3 levels using frontal-view videos acquired with a single smartphone. By leveraging coordination-informed engineered features and explainable artificial intelligence, the framework enables transparent interpretation of movement performance beyond isolated joint deviations. The proposed workflow uses smartphones for standardized video acquisition, while performance screening is achieved through machine learning. Given its lightweight feature design, this framework has potential for future implementation on modern smartphones and may support rehabilitation planning and injury-prevention strategies in sports and clinical settings.