Beyond self-reports after anterior cruciate ligament injury – machine learning methods for classifying and identifying movement patterns related to fear of re-injury
Abdolamir Karbalaie, Andrew Strong, Tomas Nordström, Lina Schelin, Jonas Selling, Helena Grip, Kalle Prorok, Charlotte K. Häger
Abstract
Anterior cruciate ligament (ACL) tears are prevalent career-ending sports injuries. A barrier to successful return to activity is fear of re-injury. Evaluating psychological readiness is however limited to insufficient self-reported assessments. We developed machine learning models using biomechanical data from standardized rebound side hops (SRSH) to objectively classify fear levels post-ACL reconstruction (ACLR) and identify key biomechanical variables. Sixty individuals with ACLR and 47 controls performed up to 10 side hops per leg. Kinematic and kinetic data were collected using motion capture and force platforms. ACLR participants were classified (Tampa Scale for Kinesiophobia-17) as HIGH-FEAR (n = 32) or LOW-FEAR (n = 28). Analyses involved 1D convolutional neural networks (1D CNN) and logistic regression. Integrated gradients identified influential movement variables. The 1-D CNN distinguished HIGH-FEAR versus LOW-FEAR ACLR individuals in agreement with Tampa Scale scores, achieving a mean accuracy of 75.6% (F₁ Score = 0.76, Matthews Correlation Coefficient = 0.52), which was 8.6% better than logistic regression. Influential variables included trunk tilt, hip flexion/extension, and ankle supination/pronation. Machine learning from biomechanics can identify movement linked to fear of re-injury post-ACLR, potentially informing personalised rehabilitation to mitigate fear and enhance recovery.