What question did this study set out to answer?

This research aims to develop an AI-driven system for predicting and preventing sports-related injuries using big data.

January 18, 2026

A073: Exercise and Artificial Intelligence

Key Points

This research aims to develop an AI-driven system for predicting and preventing sports-related injuries using big data.
Developed a sports injury feature database using wearable devices, motion capture systems, and clinical records.
Employed machine learning algorithms like Random Forest and Long Short-Term Memory for risk factor analysis.
Optimized model performance via A/B testing and cross-validation.
Conducted longitudinal tracking and validated interventions with a real-time feedback system.
Achieved predictive accuracy of 89.7% with the AI model, improving over traditional methods' 72.4%.
Reduced recurrence rates of chronic injuries, such as ankle instability, by 34% with personalized interventions.
Enhanced gait correction efficiency by 41% through an exoskeleton-assisted rehabilitation system.
Identified 15 high-risk movement patterns using multimodal data fusion techniques.

Abstract

The incidence of sports-related injuries continues to rise, yet traditional prevention strategies, such as empirical training modifications and static biomechanical assessments, exhibit significant limitations in dynamic risk prediction and personalized intervention. This study aims to leverage the integration of artificial intelligence (AI) and big data technologies to establish a dynamic, precise system for sports injury prediction and prevention, thereby reducing injury rates and enhancing rehabilitation strategies. A comprehensive sports injury feature database was developed by integrating multi-source data, including physiological parameters collected from wearable devices, biomechanical data from video motion capture systems, and clinical rehabilitation records. Machine learning algorithms, including Random Forest and Long Short-Term Memory (LSTM) neural networks, were employed to identify and analyze key risk factors for injuries, such as peak joint load and muscle fatigue index. Model performance was further optimized through A/B testing and cross-validation. Longitudinal cohort tracking of athletes was conducted, and the effectiveness of intervention measures was validated through a real-time feedback system. The AI-driven model demonstrated a predictive accuracy of 89.7% (AUC = 0.93), representing a substantial improvement over traditional methods, which achieved only 72.4% accuracy. For chronic injuries such as ankle instability, personalized intervention protocols led to a 34% reduction in recurrence rates. The exoskeleton-assisted rehabilitation system enhanced gait correction efficiency by 41% while simultaneously reducing recovery time. Furthermore, multimodal data fusion techniques successfully identified 15 high-risk movement patterns, providing objective, data-driven insights to optimize training regimens. The integration of AI and big data technologies facilitates the early detection and precise prevention of sports injuries through dynamic monitoring and sophisticated pattern recognition. Despite these advances, challenges related to data privacy, interdisciplinary collaboration, and algorithm transparency remain. Future research should focus on expanding the diversity of sample populations and exploring the potential application of AI in sports psychology interventions. This innovative technological framework offers transformative solutions for injury prevention and management in both elite athletics and general fitness contexts.

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