A Multi-Dimensional Joint Quantitative Evaluation Method for Table Tennis Techniques Based on OpenPose and YOLO

Traditional table tennis technique evaluation relies heavily on coaches’ subjective judgment, which limits the objectivity, consistency, and scalability of instructional feedback. To address this problem, this study proposes a multi-dimensional joint quantitative evaluation method for table tennis techniques based on OpenPose and YOLOv8 using consumer-grade high-frame-rate video. A total of 50 participants were recruited and divided into a high-level group and a low-level group. Standardized forehand drive and backhand push tasks were recorded using a synchronized dual-view camera setup. OpenPose was used to extract upper-body keypoint trajectories for kinematic analysis, while YOLOv8 was employed to detect and track the ball, racket, and net for outcome-related feature extraction. Based on these data, seven core indicators covering movement stability, coordination, timing, smoothness, and hitting effectiveness were selected to construct a quantitative scoring model, which was further optimized by ridge regression and validated against expert ratings from three senior athletes/coaches. The results show significant between-group differences in multiple technical dimensions, including impact accuracy, smoothness, trajectory consistency, and limb coordination (p<0.001). The model score was strongly correlated with expert ratings (r=0.882, p<0.001) and demonstrated high reliability (ICC=0.915). These findings indicate that the proposed framework can provide a low-cost, non-invasive, and practically effective solution for intelligent table tennis teaching, technical diagnosis, and skill-level evaluation.