Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dietary <i>Bacillus subtilis</i> Improves Growth Performance, Digestive Enzyme Activity, Antioxidant and Inflammatory Responses, and Gut Microbiota in Juvenile GIFT (<i>Oreochromis niloticus</i>).

Animals : an open access journal from MDPI·2026
Same author

Maize-recruited Bacillus velezensis inhibit Fusarium oxysporum by directly secreting antimicrobial metabolites.

Microbiological research·2026
Same author

Micro-CT dynamic changes and molecular mechanisms in a pulmonary nodule mouse model induced by Benzo[a]pyrene and lipopolysaccharide.

Journal of thoracic disease·2026
Same author

Production of Tellurene Nanoribbons.

Small methods·2026
Same author

An adaptive attention U-network for recognizing ultrasound images.

The Journal of international medical research·2026
Same author

Neuromechanical characteristics of breaststroke in adolescent swimmers across different performance levels: a comparative analysis.

Frontiers in bioengineering and biotechnology·2026

Related Experiment Video

Updated: Jul 7, 2026

Automated Analysis of C. elegans Swim Behavior Using CeleST Software
08:47

Automated Analysis of C. elegans Swim Behavior Using CeleST Software

Published on: December 7, 2016

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

Gongju Liu1, Haidan Liang2, Miao Zhou3

  • 1Key Laboratory of Aquatic Sports Science, General Administration of Sport, Zhejiang College of Sports, Hangzhou, Zhejiang, 311200, China.

BMC Sports Science, Medicine & Rehabilitation
|July 5, 2026
PubMed
Summary

This study introduces an automated vision-language framework for real-time swimming analysis. It provides objective kinematic profiling and coaching diagnostics, reducing manual effort in elite training.

Keywords:
Computer VisionKinematic AnalysisLarge Language ModelObject Detection

More Related Videos

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:52

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Web-based Clinician Guide to Record Compatible Video of Standardized Drinking Task Kinematics for Computer Vision Analysis
07:28

Web-based Clinician Guide to Record Compatible Video of Standardized Drinking Task Kinematics for Computer Vision Analysis

Published on: November 28, 2025

Related Experiment Videos

Last Updated: Jul 7, 2026

Automated Analysis of C. elegans Swim Behavior Using CeleST Software
08:47

Automated Analysis of C. elegans Swim Behavior Using CeleST Software

Published on: December 7, 2016

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:52

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Web-based Clinician Guide to Record Compatible Video of Standardized Drinking Task Kinematics for Computer Vision Analysis
07:28

Web-based Clinician Guide to Record Compatible Video of Standardized Drinking Task Kinematics for Computer Vision Analysis

Published on: November 28, 2025

Area of Science:

  • Sports Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Manual kinematic analysis in swimming is time-consuming and not immediate enough for elite training.
  • Current methods lack the efficiency needed for real-time feedback during high-level athletic preparation.

Purpose of the Study:

  • To develop an automated vision-language framework for real-time kinematic analysis in swimming.
  • To provide immediate coaching diagnostics and performance profiling for swimmers.

Main Methods:

  • Utilized YOLOv11 for object detection with a splash-injection strategy to manage aquatic occlusion.
  • Extracted kinematic metrics (velocity, distance) using homography transformation.
  • Integrated the DeepSeek-V3 Large Language Model for automated pedagogical feedback and report generation.

Main Results:

  • Achieved a mean Average Precision (mAP@0.5) of 94.64% in challenging dynamic water conditions.
  • Successfully tracked swimmers through water turbulence.
  • Synthesized quantitative kinematic data into natural language assessments of pacing and fatigue.

Conclusions:

  • The open-source framework significantly decreases the manual workload associated with performance analysis.
  • Combines computer vision and automated reporting for a scalable, objective tool.
  • Suitable for daily swim training and technical evaluation in competitive swimming.