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ORIGINAL RESEARCH article

Front. Educ.
Sec. Digital Learning Innovations
Volume 9 - 2024 | doi: 10.3389/feduc.2024.1385205
This article is part of the Research Topic Artificial intelligence (AI) in the complexity of the present and future of education: research and applications View all 8 articles

Deep Learning Enabled Exercise Monitoring System for Sustainable Online Education of Future Teacher-Trainers

Provisionally accepted
Nurlan Omarov Nurlan Omarov 1,2Bakhytzhan Omarov Bakhytzhan Omarov 2*Quwanishbay Mamutov Quwanishbay Mamutov 3Zhanibek Kissebayev Zhanibek Kissebayev 4Almas Anarbayev Almas Anarbayev 2Adilbay Tastanov Adilbay Tastanov 4Zhandos Yessirkepov Zhandos Yessirkepov 1
  • 1 Al-Farabi Kazakh National University, Almaty, Kazakhstan
  • 2 International University of Tourism and Hospitality, Turkistan, Kazakhstan
  • 3 Nukus branch of the Institute for Retraining and Professional Development of Specialists in Physical Education and Sport, Nukus, Uzbekistan, Nukus, Uzbekistan
  • 4 Kazakh Academy of Sport and Tourism, Almaty, Kazakhstan

The final, formatted version of the article will be published soon.

    In recent years, the importance of effective training methods for future physical education teacher-trainers has grown significantly, particularly in the context of online education. This research introduces a pioneering Deep Learning Enabled Exercise Monitoring System aimed at enhancing the online education experience for these trainers. The study employs a skeleton-based approach utilizing the PoseNet model to monitor and provide real-time feedback on physical exercises such as pull-ups, push-ups, sit-ups, squats, and bicep workouts. The system achieves a remarkable accuracy rate of 99.8% by analyzing key skeletal points extracted from video frames, addressing the challenge of ensuring correct exercise execution without physical supervision—a common issue in remote learning environments. To validate the system's effectiveness, data was collected through a series of controlled experiments involving various exercises. The system's design focuses on low-resource requirements, making it accessible and sustainable for diverse educational contexts. The findings demonstrate the system's potential to revolutionize online physical education by offering a balance of technological innovation and educational utility. This research not only elevates the quality of training for future educators but also contributes to the broader field of sustainable digital education technologies.

    Keywords: online education1, distance learning2, exercise monitoring3, physical eductaion4, deep learning5, computer vision6, artificial intelligence7

    Received: 12 Feb 2024; Accepted: 18 Jul 2024.

    Copyright: © 2024 Omarov, Omarov, Mamutov, Kissebayev, Anarbayev, Tastanov and Yessirkepov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Bakhytzhan Omarov, International University of Tourism and Hospitality, Turkistan, Kazakhstan

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.