The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Outstanding Reviewers

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • ICONS_BA 2025

Computer Vision Conference (CVC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • RSS Feed

DOI: 10.14569/IJACSA.2024.0150956
PDF

Exploring the Application of Neural Networks in the Learning and Optimization of Sports Skills Training

Author 1: Dazheng Liu

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 9, 2024.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Sports skills training is a crucial component of sports education, significantly contributing to the development of athletic abilities and overall physical literacy. It is essential to utilized neural networks to optimize traditional training methods that are inefficient and rely on subjective assessments. This paper develops methods for sports action recognition and athlete pose estimation and prediction based on deep neural networks. Given the complexity and rapid changes in sports skills, we propose a multi-task framework-based HICNN-PSTA model for jointly recognizing sports actions and estimating human poses. This method leverages the advantages of Convolution and Involution operators in computing channel and spatial information to extract sports skill features and uses a decoupled multi-head attention mechanism to fully capture spatio-temporal information. Furthermore, to accurately predict human poses to avoid potential sports injuries, this paper introduces an MS-GCN prediction model based on the multi-scale graph. This method utilizes the constraints between human body key points and parts, dividing the 2D human pose into different levels, significantly enhancing the modeling capability of human pose sequences. The proposed algorithms have been thoroughly validated on a basketball skills dataset and compared with various advanced algorithms. Experimental results sufficiently demonstrate the effectiveness of the proposed methods in sports action recognition and human pose estimation and prediction. This research advances the application of deep neural networks in the field of sports training, providing significant reference value for related studies.

Keywords: Deep neural network; action recognition; 2D pose prediction; pose estimation; sports skill training; attention mechanism

Dazheng Liu. “Exploring the Application of Neural Networks in the Learning and Optimization of Sports Skills Training”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.9 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150956

@article{Liu2024,
title = {Exploring the Application of Neural Networks in the Learning and Optimization of Sports Skills Training},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150956},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150956},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
author = {Dazheng Liu}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

Computer Vision Conference (CVC) 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

Artificial Intelligence Conference 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computer Vision Conference
  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

The Science and Information (SAI) Organization Limited is a company registered in England and Wales under Company Number 8933205.