A Motion Injury Detection and Prevention System Based on Image Recognition and Deep Learning

With the popularization of sports and fitness activities, how to effectively monitor and prevent sports injuries has become an important challenge. This article proposes a sports injury detection and prevention system. Firstly, this system collects real-time motion image data of athletes through high-definition cameras and uses YOLOv5 (You Only Look Once v5) for efficient object detection and action recognition. Then, combining long short-term memory networks (LSTM) to analyze time series data during the motion process. Finally, multimodal data fusion technology was introduced to comprehensively analyze the data. The experimental results show that the system achieves an accuracy of 88% when processing 5000 samples. In terms of real-time performance, the system has a response time of 0.60 seconds under 5000 samples. In the above data conclusions, the proposed system outperforms traditional models in terms of accuracy, real-time performance, and computational resource consumption in sports injury detection, demonstrating its effectiveness and feasibility in practical applications.