Enhancing Tennis Serve Performance Through AI Video Analysis: Acceleration and Accuracy Optimization

This study investigated the impact of AI-driven video analysis on the serve performance of national university elite male tennis players, focusing on speed and accuracy optimization. Using a pre-test/post-test design, 46 participants (23 experimental, 23 control) underwent an 8-week AI-guided training intervention. The experimental group received individualized biomechanical recommendations via 2D motion analysis using OpenPose. Results showed serve speed increased from 160.0 ± 6.0 km/h to 163.0 ± 5.8 km/h (p = 0.032) and accuracy from 65.0 ± 8.0% to 72.0 ± 7.0% (p < 0.001) in the experimental group, with significant improvements in shoulder rotation, elbow velocity, racket speed, and center of mass displacement (p < 0.05). The control group showed no significant changes. Knee flexion, toss height, trunk rotation, and racket angle remained unchanged (p > 0.05). Findings suggest AI video analysis effectively enhances serve performance, particularly accuracy, with low-cost scalability, though speed gains were modest, indicating a need for longer interventions. Future research could explore 3D analysis and broader populations.