Abstract
In recent years, sports injuries in professional tennis players have gradually increased and sports injuries will break the sports training system and affect the long-term growth of new tennis players. Avoiding athlete injuries has become an important factor in improving training quality and game performance and ensuring the sustainable development of young tennis players’ competitiveness. Therefore, this article will use the RBF neural network algorithm and cluster analysis method to establish a tennis sports injury risk early warning model and finally establish a tennis sports injury risk early warning system so that tennis players can reduce their injuries. In this article, we use the questionnaire survey method, expert interview method, mathematical statistics method, and logical analysis method to investigate and analyze the results of training injuries of Chinese tennis players and coaches. The experimental results in this article show that among 48 tennis players of different ages, who are participating in formal training and tennis competitions, 15 young tennis players have been injured more than 6 times, accounting for 31.2% of the total; 20 have been injured 3 to 6 times, accounting for 41.7% of the total; 9 of them have been injured several times, accounting for 18.8% of the total; and 4 have been injured, accounting for 8.3% of the total. After using the tennis sports injury risk warning system based on the algorithm of RBF neural network in mobile computing, the tennis sports injury rate has dropped to 5%. It can be seen that the system has high feasibility and practicability.
Highlights
A total of 50 young tennis players and 10 tennis coaches from the four units of Zibo Sports School in Shandong Province, Fuxin Tennis Amateur Sports School in Liaoning Province, Shenyang Xingguo Tennis Club in Liaoning Province, and Anshan Sports School in Liaoning Province are selected as subjects of investigation
Since participating in formal training and tennis competitions, 15 young tennis players have been injured more than 6 times, accounting for 31.2% of the total; 20 have been injured 3–6 times, accounting for 41.7% of the total; and 9 have been injured several times, accounting for 18.8% of the total; and 4 have been injured, accounting for 8.3% of the total (Figure 5)
Based on the analysis of track and field sports injury factors, an intelligent early warning model based on RBF neural network is proposed
Summary
For a long time, the development of competitive tennis in our country has faced arduous problems, and many people have suffered various degrees of sports injuries. E occurrence of sports injuries destroys the sports training system and affects the long-term growth of new tennis players. Young tennis players must receive years of systematic scientific training. Long-term injuries bring heavy psychological burdens to young tennis players, which reduce their desire for training and competition and significantly reduce their training results. Sports injuries have a major impact on the professional development of young tennis players and seriously waste the country’s human and financial resources. Knowing how to avoid athletes’ injuries during training and competitions is very important for young tennis players to improve the quality of training and sports performance and ensure the sustainable development of their competitiveness.
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