Sports-Injury Encephalopathy

Diagnostic Value of Specialist Systems in Sports Knee Injuries

In order to explore the diagnostic effect of the expert system in knee sports injury, a method of diagnostic value of expert system in sports knee sports injury is proposed. This paper mainly takes 200 professional football players in sports higher vocational colleges as the research object. There are 146 male athletes and 44 female athletes; we establish a football injury ontology knowledge base and use a reasoning engine to build an intelligent expert system diagnosis system, allowing users to quickly discover diseases, accurately diagnose injuries, and obtain the best means of rehabilitation. Through the investigation, it can be seen that the body parts caused by football injuries are more complex, and the types of injuries in each part are also different. Therefore, it is particularly important to establish an intelligent retrieval system with convenient query and clear diagnosis by the expert system. With the birth and development of computer and artificial intelligence technology, the development of artificial intelligence expert systems in the medical field has become a reality. The construction of this system will have theoretical and practical significance and application value.

Image Recognition of Sports Athletes’ High-Intensity Sports Injuries Based on Binocular Stereo Vision

Sports athletes are prone to certain injuries during high-intensity exercise training. In the process of treating an injury, images of the injury site need to be collected and identified. However, the traditional recognition method cannot effectively extract the features of the image. At the same time, it ignores the optimization of the damage image recognition results, resulting in low recognition accuracy and poor efficiency. Binocular stereo vision technology can quickly and accurately detect moving objects. Therefore, in order to more accurately identify high-intensity sports injury images, this study takes the high-intensity sports injury images as the basic research object. Several processes of image processing based on binocular stereo vision are analyzed, and the vulnerable parts of the body in high-intensity sports are also studied. Finally, the method in this study is verified. The experimental results show that the method proposed in this study reduces the average error rate by 0.19% compared with the traditional recognition method. It can effectively identify and detect injury images, thereby improving the accuracy and stability of sports injury image identification. The  identification time is also shortened accordingly, which has certain practicability and feasibility. In addition, the binocular camera used in this study has high accuracy, and the obtained images of sports injuries are of good quality, which lays a foundation for image detection and recognition.