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Design of Sports Training Information Platform Based on Virtual Reality

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1147)

Abstract

With the rapid progress of science and technology, especially computer technology and sensor technology, virtual reality technology has gradually entered people’s vision and is a new technology that has attracted people’s attention in recent years. At present, the application of virtual reality is gradually widespread, involving entertainment, education, military and other fields. In recent years, as the country attaches more importance to sports, the requirements of sports training are becoming higher. In order to continuously improve the effect of physical training, physical training is gradually combined with virtual reality technology, and it is gradually trying to realize the design of the information platform of physical training on its basis. However, there are many problems in the design of sports training information platform. How to realize the effective application of virtual reality in the design of sports training information platform and promote the realization of platform design as soon as possible has become the focus of people’s attention. Based on virtual reality and canny edge detection algorithm, this paper proposes a specific way of designing sports training information platform. On the one hand, it speeds up the construction of the information platform, and on the other hand, it provides a certain theoretical basis for future research on relevant aspects.

Keywords

Virtual reality Physical training Information platform design Canny edge detection algorithm 

References

  1. 1.
    Gang, L., Shimin, J.: Research on construction and management of the information platform of the sports training monitoring laboratories in sports research institutes. Contemp. Sports Technol. 33(2), 124–127 (2017)Google Scholar
  2. 2.
    Wu, H.J., Li, X.K., Zhao, H.Y.: A research on sports training auxiliary system based on cloud computing. Appl. Mech. Mater. 37(15), 397–400 (2017)Google Scholar
  3. 3.
    Chang, E.C.H., Chu, C.H., Karageorghis, C.I.: Relationship between mode of sport training and general cognitive performance. J. Sport Health Sci. 6(1), 89–95 (2017)CrossRefGoogle Scholar
  4. 4.
    Santos, S., Jiménez, S., Sampaio, J.: Effects of the Skills4Genius sports-based training program in creative behavior. PLoS ONE 12(2), 172 (2017)Google Scholar
  5. 5.
    Hao, Y.: Platform design of sports meeting management system for regular colleges and universities based on B/S structure. Wirel. Pers. Commun. 102(2), 1223–1232 (2018)CrossRefGoogle Scholar
  6. 6.
    Hui, Q.: Motion video tracking technology in sports training based on Mean-Shift algorithm. J. Supercomputing 12(7), 551–556 (2019)Google Scholar
  7. 7.
    Lee, H.T., Kim, Y.S.: The effect of sports VR training for improving human body composition. EURASIP J. Image Video Process. 20(1), 234–238 (2018)Google Scholar
  8. 8.
    Laby, D.M.: Case report: use of sports and performance vision training to benefit a low vision patient’s function. Optometry Vis. Sci. 95(9), 12–15 (2018)CrossRefGoogle Scholar
  9. 9.
    Jing, S.G., Wan, W.Y.: Analysis of lower limbs dynamics and its application in the sports training based on computer vision. Appl. Mech. Mater. 12(17), 513–517 (2018)Google Scholar
  10. 10.
    Capranica, L., Millard-Stafford, M.L.: Youth sport specialization: how to manage competition and training. Int. J. Sports Physiol. Perform. 6(4), 572–579 (2017)CrossRefGoogle Scholar
  11. 11.
    Pod1, T.R., Zmuda, J.M., Yurgalevith, S.M.: Lipoproteinlipase activity and plasma triglyceride clearance are elevated in endurance-trained wome. Metabolism 43(7), 8082–8131 (2017)Google Scholar
  12. 12.
    Stricker, P.R.: Sports training issues for the pediatric athlete. Pediatric Clin. North Am. 49(4), 793–802 (2017)CrossRefGoogle Scholar
  13. 13.
    Hey, J., Carter, S.: Pervasive computing in sports training. IEEE Perv. Comput. 4(3), 54 (2017)CrossRefGoogle Scholar
  14. 14.
    Fister, I., Rauter, S., Yang, X.-S.: Planning the sports training sessions with the bat algorithm. Neurocomputing 149(7), 993–1002 (2017)Google Scholar
  15. 15.
    Neilson, V., Ward, S., Hume, P.: Effects of augmented feedback on training jump landing tasks for ACL injury prevention: a systematic review and meta-analysis. Phys. Ther. Sport: Official J. Assoc. Chartered Physiotherapists Sports Med. 39(63), 126–136 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Jilin Engineering Normal UniversityChangchunChina

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