Sports training injuries and prevention measures using big data analysis
Abstract
This work explores the application of big data technology in monitoring sports training injuries, emphasizing the biomechanical principles underlying injury mechanisms to enhance the accuracy of injury prediction and provide scientific prevention measures. It collects training data from professional sports teams using big data technology and constructs a Bi-directional Long Short-Term Memory (BiLSTM)—Residual Network (ResNet) model through deep learning techniques. In this model, the BiLSTM module captures the temporal sequence features of sports data, while the ResNet module improves the model’s expressiveness and stability through residual learning. To establish a clearer connection with mechanobiology, the study discusses the mechanical forces involved in sports injuries, including impact forces, torsional stresses, and their effects on tissues at the cellular level. By integrating biomechanical insights with big data analytics, the research aims to provide a comprehensive understanding of how mechanical stressors contribute to injury risk. The performance of the proposed model in predicting sports injury risks is evaluated, showing an accuracy of 95.72%, a precision of 91.59%, a recall of 85.40%, and an F1 score of 88.56%, significantly outperforming existing traditional models and other comparison algorithmsTherefore, the proposed model demonstrates exceptional performance in improving the accuracy of sports injury prediction and providing personalized prevention measures, offering experimental references for the intelligent development of the sports field by bridging sports science and biomechanics.
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