A Computer Vision Framework on Biomechanical Analysis of Jump Landings

High-intensity activities in sports like basketball can result in fatigue without proper recovery. This study introduces a collaborative framework that leverages Computer Vision (CV) and Machine Learning for evaluating jump landings and predicting athletic readiness by modelling Countermovement Jumps (CMJs) biomechanical aspects. Seventeen female collegiate basketball athletes of Sacred Heart University (SHU), CT, USA, participated in weekly CMJs over a 26-week season. Through CV-driven semantic analysis of videos, the framework identifies the crucial initial contact and maximum flexion point during jump landings and extracts kinetic and kinematic features of the lower extremities. Next, an inferential analysis is conducted to understand the relationship between these features and the CMJ-driven reactive strength index modified (RSI_mod) score, which measures fatigue and athletic readiness. An XGBoost regressor, trained on the past week’s data, then predicted the RSI_mod score for the following week, which resulted in an MSE of 0.020 and an R² of 0.892. Using SHapley Additive exPlanations (SHAP), the framework offers interpretable feedback, aiding coaches in creating personalised training programs and optimising athletic performance while minimising injury risks.