Value evaluation of knee joint sports injury detection model-aided diagnosis based on machine learning

Abstract

Athletes often suffer from knee joint injuries because they often use the knee joint to exert force during training. This paper aims to analyze and discuss the auxiliary diagnosis of the knee joint sports injury detection model based on machine learning. This paper expounds the treatment method of knee joint injury, and proposes a machine learning algorithm. On the basis of this research, the auxiliary diagnosis experiment of the knee joint sports injury detection model is analyzed. The experimental results show that after 3 months of machine learning-based rehabilitation training, there is a significant difference in the duration of the balance pad before and after the table tennis players practice. The duration of the athletes on the balance mat has increased, and the increase is relatively large. Among them, the average duration of female athletes on the balance mat increased from 75.5 seconds before training to 141.9 seconds after training, while the average duration of male athletes on the balance mat increased from 66.7 seconds before training to 136.8 seconds after training. Studies have shown that machine learning-based rehabilitation physical training can significantly improve athletes' endurance on balance mats and can improve knee function scores. In summary, machine learning-based rehabilitation physical training can effectively improve knee joint injuries.