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Optimization of a Sports Activity Development Model Using Artificial Intelligence under New Curriculum Reform

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  • Taofeng Liu

    (School of Physical Education Institute (Main Campus), Zhengzhou University, No. 100 Science Avenue, Zhengzhou 450001, China
    Department of Physical Education, Sangmyung University, Seoul 390-711, Korea)

  • Dominika Wilczyńska

    (Faculty of Physical Culture, Gdansk University of Physical Education and Sport, Kazimierza Górskiego 1, 80-336 Gdańsk, Poland)

  • Mariusz Lipowski

    (Faculty of Physical Culture, Gdansk University of Physical Education and Sport, Kazimierza Górskiego 1, 80-336 Gdańsk, Poland)

  • Zijian Zhao

    (School of Physical Education Institute (Main Campus), Zhengzhou University, No. 100 Science Avenue, Zhengzhou 450001, China)

Abstract

The recent curriculum reform in China puts forward higher requirements for the development of physical education. In order to further improve students’ physical quality and motor skills, the traditional model was improved to address the lack of accuracy in motion recognition and detection of physical condition so as to assist teachers to improve students’ physical quality. First, the physical education teaching activities required by the new curriculum reform were studied with regard to the actual needs of China’s current social, political, and economic development; next, the application of artificial intelligence technology to physical education teaching activities was proposed; and finally, deep learning technology was studied and a human movement recognition model based on a long short-term memory (LSTM) neural network was established to identify the movement state of students in physical education teaching activities. The designed model includes three components: data acquisition, data calculation, and data visualization. The functions of each layer were introduced; then, the intelligent wearable system was adopted to detect the status of students and a feedback system was established to assist teaching; and finally, the dataset was constructed to train and test the designed model. The experimental results demonstrate that the recognition accuracy and loss value of the training model meet the practical requirements; in the algorithm test, the motion recognition accuracy of the designed model for different subjects was greater than 97.5%. Compared with the traditional human motion recognition algorithm, the designed model had a better recognition effect. Hence, the designed model can meet the actual needs of physical education. This exploration provides a new perspective for promoting the intelligent development of physical education.

Suggested Citation

  • Taofeng Liu & Dominika Wilczyńska & Mariusz Lipowski & Zijian Zhao, 2021. "Optimization of a Sports Activity Development Model Using Artificial Intelligence under New Curriculum Reform," IJERPH, MDPI, vol. 18(17), pages 1-13, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:17:p:9049-:d:623350
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    References listed on IDEAS

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    1. Hyun Suk Lee & Junga Lee, 2021. "Applying Artificial Intelligence in Physical Education and Future Perspectives," Sustainability, MDPI, vol. 13(1), pages 1-16, January.
    2. Yenchun Jim Wu & Chih-Hung Yuan & Chia-I Pan, 2018. "Entrepreneurship Education: An Experimental Study with Information and Communication Technology," Sustainability, MDPI, vol. 10(3), pages 1-13, March.
    3. Maricarmen Almarcha & Natàlia Balagué & Carlota Torrents, 2021. "Healthy Teleworking: Towards Personalized Exercise Recommendations," Sustainability, MDPI, vol. 13(6), pages 1-12, March.
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    1. Shadi Atalla & Mohammad Daradkeh & Amjad Gawanmeh & Hatim Khalil & Wathiq Mansoor & Sami Miniaoui & Yassine Himeur, 2023. "An Intelligent Recommendation System for Automating Academic Advising Based on Curriculum Analysis and Performance Modeling," Mathematics, MDPI, vol. 11(5), pages 1-25, February.

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