Recognition of Volleyball Player's Arm Motion Trajectory and Muscle Injury Mechanism Analysis Based upon Neural Network Model.

Jinxiang Zhao,Zengli Li

doi:10.1155/2022/8114740

Abstract

At present, in sports training for volleyball, it still mainly depends on the personal experience of the coach. Training costs are high, and the quality is difficult to maintain stable. Even with the introduction of training assistance software, it is often necessary to manually enter complex data, and the research samples are mostly single individuals. Serving is one of the basic and important technical movements of volleyball, and its standardization is of great significance to the stable performance of the scene. This article proposes an analysis of the volleyball player's arm trajectory based on the background of human posture recognition and analysis, based on the neural network model. The changes in the angles of the shoulders, elbows, and wrist when serving the ball reflect the different trajectories of the arm. Experiments show that the height of the throwing arm from the ground accounts for 98% of the height. The horizontal angle of the throwing arm at the moment the ball leaves the hand is positively correlated with the throwing time and height, and the reasonable trajectory has an impact on the stability of the throwing ball. The closer the trajectory of the tossing arm is to the vertical, the more stable the tossing is.

Highlights

At present, in sports training for volleyball, it still mainly depends on the personal experience of the coach
Serving is one of the basic and important technical movements of volleyball, and its standardization is of great significance to the stable performance of the scene. is article proposes an analysis of the volleyball player’s arm trajectory based on the background of human posture recognition and analysis, based on the neural network model. e changes in the angles of the shoulders, elbows, and wrist when serving the ball reflect the different trajectories of the arm
Zhang J proposed a bolt-shaped friction nanogenerator (BS-TENG), which can be used for trajectory detection during rock climbing training. e peak value of the output voltage pulse is between 4 and 7 V, which has a strong signal-to-noise ratio and anti-interference ability [2]

Summary

Neural Network Based on Volleyball Arm Trajectory Recognition

E classic convolutional neural network CNN classification network is usually the fully connected layer at the end [9]. This will lose the spatial information of the feature [10, 11]. Is section expands the hidden layer structure diagram of the RNN as shown, using q as the sample input. At this time, the memory of the sample at time s is represented by Ts, so Ts f(M · Ts−1 + N · qs).

96 Figure 2

Volleyball Serve Experiment Based on Multilayer Features of Neural Network

Attitude Preprocessing

Volleyball Arm Trajectory Based on Neural Network

Findings

Method