Injury Risk and Recovery: Machine Learning Applied to Biomechanical and Physiological Markers to Flag Overtraining and Injury Likelihood

Abstract

Introduction and Objective. Athlete monitoring has increasingly shifted from retrospective injury 
surveillance toward longitudinal, data-driven assessment of training tolerance, recovery and injury-related 
risk. The combination of wearable sensors, force-plate testing, movement analysis and physiological 
monitoring generates multimodal datasets that may contain early signals of altered recovery and changes in 
movement or workload patterns. This narrative review examines how machine-learning (ML) methods can 
integrate biomechanical and physiological markers to support injury-risk assessment, overtraining 
monitoring and recovery prediction. Material and method. The review was organized around three major 
predictive targets: non-contact musculoskeletal injury, overtraining or maladaptation, and short-term 
recovery status. Particular attention was given to ground-reaction forces, joint kinematics, movement 
asymmetry, heart-rate variability, resting heart rate, perceived exertion, sleep and internal- and external-load 
variables. The principal ML approaches considered were regularized regression, support vector machines, 
tree-based ensembles, and temporal models including recurrent neural networks. Model development, 
feature construction, class imbalance, athlete-level validation, calibration and interpretability were 
considered as key methodological requirements. Results. ML can combine multiple weak and correlated 
signals to identify patterns that may be difficult to detect using isolated thresholds. Wearable-derived 
workload and physiological data are particularly suitable for longitudinal monitoring, while force-plate and 
kinematic measurements provide complementary information about neuromuscular and mechanical 
changes. However, reported performance varies substantially between studies because of differences in 
sample size, injury definitions, outcome labels, sensor technology, feature engineering and validation 
procedures. Small athlete cohorts, repeated observations, class imbalance and limited external validation 
remain major sources of uncertainty. Conclusions. ML provides a useful decision-support framework for 
individualized injury and recovery monitoring, but predictive accuracy should not be interpreted as 
diagnostic validity. Robust implementation requires athlete-level or temporal validation, transparent 
preprocessing, calibrated predictions, external testing and integration of model output with clinical and 
coaching expertise.