Method for Recognition of Tennis Error Training Action Based on Artificial Intelligence Technology

Artificial intelligence (AI) has brought great changes to the traditional sports industry. In order to solve the shortcomings of current recognition methods for tennis error training and obtain better recognition effect, we constructed a recognition method based on AI technology. A random projection algorithm was used to reduce the dimension of feature vectors, a CNN (convolutional neural network) model was used to learn training samples after dimension reduction, and a recognition model of wrong tennis training actions was constructed. The preprocessing of data included converting the original Cartesian coordinate system into a cylindrical coordinate system and normalizing the time of a skeletal motion sequence. Experiments show that the recognition accuracy of this model on the NTU-RGB+D dataset can reach 95.34%. The recognition accuracy of this model on the UTD-MHAD dataset can reach 94.12%. Compared with another model, the accuracy of this model was improved, which verified the superiority of this model. It can provide some technical support for the recognition of wrong tennis training actions and improve the tennis teaching effect and students’ learning level. Copyrights © 2024 The Institute of Electronics and Information Engineers.