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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月6日

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  • Gongju Liu,
  • Haidan Liang,
  • Miao Zhou,
  • Shiqing Zhang,
  • Yike Wang,
  • Liangliang Lou

摘要

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.