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Document Type

Abstract

Publication Date

1-14-2026

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.

DOI

https://doi.org/10.18122/ijpah.5.1.73.boisestate

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