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Research Article

Analysis and Improvement of the Application of Playground Sports Posture Detection Technology in Physical Education Teaching and Training

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  • @ARTICLE{10.4108/eetpht.10.5161,
        author={Jie Xu},
        title={Analysis and Improvement of the Application of Playground Sports Posture Detection Technology in Physical Education Teaching and Training},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2024},
        month={3},
        keywords={sports posture detection, physical education and training, snow melting optimisation algorithm, deep extreme learning machine},
        doi={10.4108/eetpht.10.5161}
    }
    
  • Jie Xu
    Year: 2024
    Analysis and Improvement of the Application of Playground Sports Posture Detection Technology in Physical Education Teaching and Training
    PHAT
    EAI
    DOI: 10.4108/eetpht.10.5161
Jie Xu1,*
  • 1: Jiangxi Technical College of Manufacturing
*Contact email: 13607911976@163.com

Abstract

  INTORDUCTION: The goal of human posture detection technology applied in the field of sports is to realise the indexing of sports norms, to provide scientific guidance for training and teaching, which is of great significance to improve the quality of sports. OBJECITVES: Aiming at the problems of incomplete features, low accuracy and low real-time performance of sports posture detection and recognition methods. METHODS: In this paper, a method of sports pose detection based on snow melting heuristic optimisation algorithm of deep limit learning machine network is proposed. Firstly, by analyzing the process of motion pose detection, extracting the feature coordinates of Blaze-Pose and Blaze-Hands key nodes, and constructing the motion pose detection recognition system; then, optimizing the parameters of the deep extreme learning machine network through the snow-melt optimization algorithm, and constructing the motion pose detection recognition model; finally, through simulation experiments and analysis, the accuracy of the proposed method's motion pose detection recognition can reach 95% and the recognition time is less than 0.01 s. RESULTS: The results show that the proposed method improves the recognition accuracy precision, robustness and real-time performance. CONCLUSION: The problem of poor generalisation, low accuracy and insufficient real-time performance of the recognition application of the motion pose detection and recognition method is solved.

Keywords
sports posture detection, physical education and training, snow melting optimisation algorithm, deep extreme learning machine
Received
2024-02-21
Accepted
2024-03-13
Published
2024-03-18
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.10.5161

Copyright © 2024 J. Xu et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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