Automated vision-language framework for kinematic profiling and performance diagnostics in competitive swimming

BACKGROUND: Manual kinematic analysis in swimming is labor-intensive and often lacks the immediacy required for elite training. This study presents an automated vision-language framework to deliver real-time kinematic profiling and coaching diagnostics. METHODS: A robust object detection pipeline using YOLOv11 was developed, incorporating a splash-injection strategy to handle aquatic occlusion. Kinematic metrics (velocity, distance) were extracted via homography transformation. To automate pedagogical feedback, the DeepSeek-V3 Large Language Model was integrated to interpret these metrics and generate structured coaching reports. RESULTS: The proposed method achieved a mean Average Precision (mAP@0.5) of 94.64% in dynamic water conditions. The system successfully tracked swimmers despite turbulence and accurately synthesized quantitative data into natural language assessments of pacing and fatigue. CONCLUSIONS: This open-source framework significantly reduces the manual burden of performance analysis. By combining computer vision with automated reporting, it offers a scalable, objective tool for daily swim training and technical evaluation.