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

A near telomere-to-telomere genome assembly of Rhodiola macrocarpa (Crassulaceae).

Scientific data·2026
Same author

The Kelch-Repeat Superfamily Gene <i>SiNL4</i> Regulates the Leaf Width in Foxtail Millet.

Plants (Basel, Switzerland)·2026
Same author

The influence of mobile phone self-expansion on psychological richness: a moderated mediation model.

Frontiers in psychology·2026
Same author

Study on the impact of multiple factors on 4-year physical fitness test results of undergraduate students at a Chinese comprehensive university: based on the mediating role of exercise habits.

Frontiers in public health·2026
Same author

The EAR-motif-containing adaptor protein ECAP enhances the assembly of PYR1-ABI1 complex to promote ABA signaling in Arabidopsis.

Molecular plant·2026
Same author

Discovery of previously undescribed specialized metabolites from the endophytic fungus Penicillium griseofulvum.

Phytochemistry·2026

Related Experiment Video

Updated: Nov 10, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.7K

Footballer Action Tracking and Intervention Using Deep Learning Algorithm.

Guanghui Yang1, Lijun Wang2, Xiaofeng Xu3

  • 1School of Physical Education, Yanshan University, Qinhuangdao, Hebei 066004, China.

Journal of Healthcare Engineering
|April 5, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for recognizing footballer movements using a convolutional neural network (CNN). This AI-powered system enhances training by accurately analyzing player actions for better performance.

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.6K
Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

10.9K

Related Experiment Videos

Last Updated: Nov 10, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.7K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.6K
Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

10.9K

Area of Science:

  • Sports Science
  • Artificial Intelligence
  • Computer Vision

Background:

  • Accurate analysis of footballer actions during training is crucial for performance improvement.
  • Human observation by coaches can be subjective and prone to errors due to the speed of play.
  • Objective, data-driven methods are needed to evaluate and refine player techniques.

Purpose of the Study:

  • To design and develop an algorithm for footballer motion and gesture recognition and intervention.
  • To utilize a convolutional neural network (CNN) for accurate analysis of player actions.
  • To provide a scientific basis for intervention in football training processes.

Main Methods:

  • Extraction of texture and HSV features from footballer posture images.
  • Construction of a dual-channel convolutional neural network (CNN) for separate feature analysis.
  • Integration of features and processing through a fully connected CNN for posture estimation.

Main Results:

  • The proposed algorithm demonstrated superior performance in experimental testing.
  • Comparative analysis on extensive datasets validated the algorithm's effectiveness.
  • Ablation studies confirmed the contribution of different components to the overall performance.

Conclusions:

  • The developed CNN-based algorithm accurately recognizes footballer motions and gestures.
  • This technology offers a reliable tool for objective assessment in football training.
  • The system facilitates scientifically-backed interventions for enhanced player development.