Interpretable Machine Learning on Simulation-Derived Biomechanical Features for Hamstrings-Quadriceps Imbalance Detection in Running.

ABSTRACT: Hamstrings-quadriceps (H-Q) imbalance represents a biomechanical marker of knee instability and injury risk in running. This in silico (simulation-based) study introduces a digital machine learning framework designed to estimate H-Q imbalance using biomechanical features derived entirely from synthetic running trials and conceptually mappable to inertial-sensor domains. Key biomechanical predictors included the dynamic hamstrings-to-quadriceps ratio (H:Qdyn), the knee moment limb symmetry index (LSI), and the early-stance co-contraction index (CCI), all standard indicators of muscular balance and coordination in sports medicine. A reduced musculoskeletal framework emulating flexor-extensor balance, limb symmetry, and co-contraction patterns generated 573 synthetic running trials for 160 vi

SUBMITTER: Manescu AM 

PROVIDER: S-EPMC12736594 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature