Evaluation of Dynamic and Static Balance Ability of Athletes Based on Computer Vision Technology

With the rapid development of computer and Internet technology, using computer vision technology to evaluate the dynamic and static balance ability of athletes has become a research hotspot. This study combines the theory of functional training of human muscle groups and delves into the relationship between body function training and dynamic and static balance ability. By developing a computer vision-based algorithm, we successfully evaluated the dynamic and static balance abilities of athletes. To verify the effectiveness and reliability of the algorithm, we used simulation training experiments and comparative analysis methods. The simulation training experiment simulated the dynamic and static balance training of athletes in a real environment, and quantitatively evaluated their balance ability through this algorithm. In addition, we also compared the changes in balance ability of athletes before and after receiving functional training to verify the effectiveness of functional training in improving balance ability. The results of these validation methods all demonstrate good consistency and accuracy, further confirming the correctness of the research results on the dynamic and static balance ability of athletes based on functional training. This study not only provides new tools and methods for athlete balance training, but also a beneficial attempt for the application of artificial intelligence in the field of sports.