CDE-Driven Time-Series Analysis of Training Load and Injury Risk Prediction for Football Players
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Abstract
Effective monitoring of training load and timely prediction of injury risk are essential for optimizing athletic performance and reducing sports-related injuries. This study proposes a Context– Data–Event (CDE)-driven framework for time-series analysis of football training loads and injury risk prediction. Multi-source physiological and motion data are collected through wearable sensing devices, including acceleration, heart rate, speed, displacement, and recovery indicators. Context-aware feature extraction is combined with long short-term memory networks to model temporal load variations, while a Bayesian risk assessment module dynamically estimates injury probability by analyzing interactions between physiological responses and training intensity fluctuations. Experimental evaluation using longitudinal training data from professional football players demonstrates high prediction accuracy and stable performance across varying load conditions. The proposed framework provides an effective solution for intelligent athlete monitoring and offers methodological references for wearable sensing technologies, physiological signal analysis, and wireless health-monitoring systems.
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