3D Convolutional Neural Networks based Movement Evaluation System for Gymnasts in Computer Vision Applications

Abstract

This study introduces an innovative Movement Evaluation System, which utilizes state of art 3D CNNs in grassland of computer vision. This system presents a significant advancement in analyzing and assessing complex gymnastic movements, providing a comprehensive understanding of both spatial and temporal dynamics. By incorporating pose estimation algorithms, it accurately identifies body joints and positions to extract detailed spatial features. The 3D CNN further captures the temporal evolution of these features, enabling a precise analysis of the fluidity, rhythm, and synchronization of gymnastic movements over time. The effectiveness of this system is evaluated through performance metrics such as precision, recall, and accuracy. Aimed at enhancing sports science and athlete training, this research offers coaches, judges, and gymnasts a sophisticated tool for objective and standardized movement assessments. It has the potential to streamline the evaluation process in gymnastics and provide constructive feedback efficiently. The integration of 3D CNNs in this system marks a paradigm shift in utilizing advanced computer vision techniques to improve the understanding and refinement of gymnastic performances.