Athlete injury detection and emergency treatment in mobile smart medical system - Zhang, Yiqiao; Zhang, Qiang; Liu, Yuhe (Frontiers in Physics, 2023)
With the development of the Internet, intelligent medical treatment has gradually become a new model of doctors and patients. As everyone knows, sports wear a lot on the body, and it is easy to cause damage to different parts of the body. In view of this situation, the use of a sports injury monitoring systems to detect injury symptoms and the timely adoption of effective treatment measures can reduce the damage to athletes' bodies caused by sports injuries. At present, many detection methods lack the support of advanced technologies and algorithms, which leads to inaccurate sports injury detection. In this paper, a mobile intelligent medical system is designed and Convolutional Neural Network (CNN) is applied to the research of injury detection of sports athletes. By analyzing the application of ML in medical treatment, an athlete injury detection method based on CNN and sensor is proposed. The method includes three parts: acquiring motion area, extracting motion injury feature, and detecting motion injury. In addition, for emergency treatment, this paper proposes a variety of image data analysis methods based on CNN to ensure the accuracy of the processing process. In this paper, the detection accuracy and false positive rate, image analysis efficiency, and classification accuracy were tested. Experiments show that the new method is effective in the detection of common damage species. The number of image analysis under the new method is clearly more than that of the traditional method, and the growth range is relatively large.The convolutional neural network-based athlete injury detection method improves the detection accuracy by 6.73% compared with conventional methods, which also provides an important reference for the future application of ML in medical treatment. Therefore, the construction and analysis of mobile intelligent medical system can effectively improve the accuracy of sports injury detection.