Application Effect of Human-Computer Interactive Gymnastic Sports Action Recognition System Based on PTP-CNN Algorithm

Abstract

With the rapid development of artificial intelligence technology, the recognition accuracy performance of traditional gymnastic sports action recognition system can no longer meet the needs of today's society. To address these problems, an improved action recognition algorithm combining Precision Time Protocal (PTP) and Convolutional Neural Networks (CNN) is proposed, and a human-computer interaction gymnastic action recognition system based on PTP-CNN algorithm is constructed. The performance test of the proposed PTP-CNN algorithm was conducted, and it was found that the accuracy of PTP-CNN algorithm was 92.8% and the recall rate was 95.2%, which was better than the comparison algorithm. The performance comparison experiments of the gymnastic action recognition system based on the PTP-CNN algorithm found that the recognition accuracy of the PTP-CNN gymnastic action recognition system was 96.3% and the running time was 3.4s, which was better than the other comparison systems. Comprehensive results can be found that the research proposed PTP-CNN recognition algorithm and improved gymnastic action recognition system can effectively improve the performance of traditional algorithms and models, which has practical application value and great application potential.