New paths to promote athletic injury prevention by integrating statistics and sports biomechanics
Abstract
Athletic injuries are a common problem in sports. Due to the insufficient processing of multimodal biomechanical data by traditional prevention strategies, personalized risk prediction cannot be achieved. To this end, this paper adopts an athletic injury prevention method based on sparse principal component analysis (SPCA) and spatio-temporal graph convolutional network (ST-GCN). The Vicon Vantage V5 3D motion capture system and the Noraxon Ultium EMG electromyography acquisition device are used to obtain the athlete’s joint angle change rate, ground reaction force (GRF) and electromyographic activity data, and the SPCA method is used to extract key biomechanical features, thereby reducing data redundancy and improving the representativeness of features. Subsequently, ST-GCN is used to construct a dynamic risk prediction model to capture the temporal changes and spatial dependencies in the motion sequence to achieve precise and efficient risk assessment. In the experimental verification, the prediction accuracy of the model reaches 95.3% when the number of features was 20, and the ability to provide risk feedback in real-time is realized to generate personalized injury prevention strategies. Studies have shown that the integration of statistics and sports biomechanics has effectively improved the efficiency of athletic injury prevention and provided new ideas for scientific and precise sports management.
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