Optimization study of badminton sports training system based on MoileNet OpenPose lightweight human posture estimation model

Abstract

The development of computer technology has effectively promoted the reform of traditional ball sports. However, the existing training methods based on computer technology are costly and difficult to promote on a large scale. Therefore, the study proposes to use deep learning techniques to improve the OpenPose tool and reduce the computational complexity of the model, in order to achieve large-scale promotion of computer-based ball sports training methods. The experimental results show that the model action score is high, 93.27 points, and the model processing time is 83 s. The model performs well in Frames Per Second index, reaching 27.5 frames/s. And it performs well in recall, precision, accuracy, and F1 value, reaching 90.13 %, 95.79 %, 89.75 %, and 89.78 %, respectively. In the comparison between the research design system and the traditional training system, the research system performs better with an average response time of only 0.96 s and an average occupancy rate of the central processor of only 20.23 %. This achievement provides an efficient and low-cost intelligent tool for badminton training, helping coaches achieve objective quantitative analysis of movements, reducing reliance on subjective experience, and providing data support for athletes’ immediate movement correction. The research successfully achieved a balance between model computational efficiency and recognition accuracy, verifying the feasibility of lightweight improvement. However, the current model mainly focuses on upper limb joints and still has shortcomings in posture analysis of other parts. In the future, research will explore the adaptability of models in complex dynamic scenarios and attempt to integrate biomechanical parameters to enhance the scientificity of feedback. In addition, the system’s functions can be extended to tactical analysis and physical fitness assessment, further promoting the digital transformation of sports training.