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An Automatic FMS Method Based on AI Technic
Sports injuries have been the most feared thing for athletes. Both chronic and acute sports injuries might shorten or even end one’s sports career. Therefore, it has always been one of the focuses of professionals in sports and medical fields. After years of research by professionals, there have been ways to prevent and treat sports injuries. It also developed occupations specialized in sports protection and treatment, such as sports protectors, physical therapists, and sports managers. With the rise of sports culture, sport is national activity now. However, not all sports teams have the ability to hire a protector, not to mention the people who exercise independently. They have difficulties to prevent it, but have medical treatments after sports injuries. Needless to say that medical resources are inconvenient in some areas. Going to the nearest rehabilitation department or physical therapy studio may take lots of time in these areas. With the rapid development of artificial intelligence (AI), the computing speed and accuracy of AI are continuously optimized. Nowadays, computers can be used for human body recognition. Once the positions of body pose can be identified with image detection, automatic detection of sports injuries and aided diagnosis will become the development direction in the future. It can not only reduce the dependence on professional resources, but also abandon the need for manual angle measurement in traditional physical therapy. This research is based on the Openpose pose detection AI model developed by Carnegie Mellon University. To make "Essentials of Corrective Exercise Training" published by the National Academy of Sports Medicine (NASM) and functional motion detection as the basis of judgment, this research combined the clinical judgment knowledge from physical therapists and rehabilitation physicians and developed a 2D image detection system for sports injuries. Mobile phone App user interface, computing server, and professional interface and database are three main parts in this system. Users will receive their health feedback and be recommend suitable rehabilitation videos from the system after it get users’ films. This research includes pose detection and analysis system. Pose detection consists of sample collection, video processing, keypoint processing, and result comparison. The detection system is comprised of system architecture, health evaluation, and the basis of video recommendation judgment.
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