Monocular 3D Tennis Serve Analysis and Rule-Based Feedback: System Design and Quasi-Experimental Validation

This study aimed to develop a monocular vision-based tennis serve analysis system and evaluate its effectiveness in beginner training. The system uses MediaPipe Pose to extract 33 body landmarks from monocular video, calculates joint angles using three-dimensional vector operations, identifies serve phases through threshold-based rules, constructs an approximate 3D pose representation using anthropometric constraints, and generates corrective feedback through a rule-based expert system. In a quasi-experimental study, 90 beginner tennis players (final n = 82) completed an 8-week intervention and were allocated to a high-frequency feedback group, a moderate-frequency feedback group, or a conventional training group. All groups showed significant improvements in Serve Quality Mastery (SQM) scores (p < 0.001). The high-frequency feedback group showed the greatest SQM improvement (SQM: +30.5 points), followed by the moderate-frequency feedback group (+24.2 points) and the conventional training group (+15.5 points). Between-group differences were significant F(2, 79) = 74.30, p < 0.001, η2 = 0.65. These findings indicate a graded pattern across the feedback-frequency groups, with more frequent system-generated feedback being associated with greater improvements in training performance. The findings support the potential use of the monocular pose-based, rule-driven feedback system as a supplementary tool for beginner tennis serve instruction.