A Novel Artificial Neural Network-Based Biomechanical Research on Elbow Injury of Tennis Serve

Abstract

Tennis is a sport with a large age span. Its technology is complex. Factors such as tennis racket and service position will affect the effect of players holding the ball, but often the service posture and strength will affect the players’ elbow tendon. Once the service posture is wrong or the strength is too strong, it will cause irreversible damage to players. Therefore, the study of artificial neural network training on tennis elbow injury is of great significance. Based on artificial neural network, this paper studies the biomechanics of elbow injury in tennis service. Firstly, this paper expounds the concept and characteristics of tennis service technology and studies the injury of tennis players’ elbow. Then this paper studies the artificial neural network technology and uses the experimental comparison method to test the force generated by the two groups of tennis players from different angles. The results show that, in the professional group, the forearm is in the internal rotation state before and after hitting the ball, while the beginner group is generally in the external rotation state. It can be inferred that, under the constant collision force, the reversal force absorbed by the beginner group in the swing process is greater than that of the professional group, which will increase the load of the supinator muscle on the antipronation, which will damage the supinator muscle, which is also one of the causes of the “tennis elbow” disease.