Sports training injury risk assessment model based on biological mechanisms and complex network analysis

Article ID: 653

Abstract

To improve the accuracy and practicality of sports training injury risk assessment (IRA), this paper constructs a model based on a complex network analysis algorithm and conducts performance comparison experiments across multiple dimensions. The research results demonstrate that the optimized model performs well in terms of risk assessment accuracy, real-time processing, robustness, adaptability, and user satisfaction. Specifically, the Area Under Curve of the Receiver Operating Characteristic Curve (AUC-ROC) of the optimized model reaches 0.928, indicating high accuracy in risk assessment. In addition to these metrics, this study includes a discussion on the biological mechanisms underlying sports injuries, emphasizing how biological signals can be integrated with the complex network analysis to enhance the model's predictive capabilities. This integration allows for a more comprehensive understanding of injury risk factors, such as muscle fatigue, joint stress, and tissue response, which are critical for effective injury prevention strategies. In the real-time experiment, the processing speed score is 4.9. In the robustness experiment, the fault recovery ability score is 4.3. In the adaptive experiment, the diversified data processing ability score is 4.5. In the user satisfaction experiment, the accuracy score of risk assessment is 4.9, and the convenience score is 5.0. These results indicate that the optimized model has significant advantages in handling complex data and adapting to changing environments. Therefore, this paper provides valuable insights for improving injury risk management and decision support in sports training by incorporating biological insights into the assessment model.