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Motion capture and evaluation system of football special teaching in colleges and universities based on deep learning

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Abstract

Football reduces body fat and increases the tone of muscle, constructs strength, flexibility, and stamina. It increases muscle power, bone as well as improvements in walking, running, and jumping.The challenging characteristics in motion capture of football teaching include lack of latency, complexity, and physical interaction analysis of sports performance. In this paper, Deep learning assisted motion capture system has been proposed to enhance bandwidth, the variability of performance, and realistic physical interactions in an accurate manner in colleges and universities. Bidirectional motion analysis is implemented to reduce animation costs and enhance motion capturing data in football events. Network evaluation management technique is introduced to recreate the intricate and realistic physical interactions of unique football teaching colleges and universities. The simulation analysis is performed based on complexity; performance, latency, and efficiency prove the proposed framework's reliability.

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Acknowledgements

Author: Yin Xiaohui, male, June 17, 1967, Han nationality, associate professor of physical Education College, Binzhou University, Shandong Province, research field: physical Education and training.

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Authors and Affiliations

  1. Scientific Research and Foreign Affairs Department, Binzhou University Institute of Physical Education, Binzhou, 256600, China

    Xiaohui Yin

  2. Department of Computer Science, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Chennai, India

    C. Chandru Vignesh

  3. Veltech University, Chennai, India

    Thanjai Vadivel

Authors
  1. Xiaohui Yin
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  2. C. Chandru Vignesh
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  3. Thanjai Vadivel
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Contributions

Conception and design of study: XY. Acquisition of data: CCV. Analysis and/or interpretation of data: TV.

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Correspondence to Xiaohui Yin.

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Yin, X., Vignesh, C.C. & Vadivel, T. Motion capture and evaluation system of football special teaching in colleges and universities based on deep learning. Int J Syst Assur Eng Manag 13, 3092–3107 (2022). https://doi.org/10.1007/s13198-021-01557-2

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  • DOI: https://doi.org/10.1007/s13198-021-01557-2

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