ArticleBMC sports science, medicine & rehabilitation2026
Automated vision-language framework for kinematic profiling and performance diagnostics in competitive swimming.
Article in BMC sports science, medicine & rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundManual 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.
methodsA 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.
resultsThe 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.
conclusionsThis 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.