Sports Science: A Comprehensive Review of Key Concepts and Applications

##plugins.themes.academic_pro.article.main##

Kamal Lohia

Abstract

Sports science is a multidisciplinary field that integrates physiology, biomechanics, psychology, and nutrition to optimize athletic performance and prevent injuries. This paper provides a comprehensive review of key concepts in sports science, including training methodologies, physiological adaptations, and the role of technology in enhancing performance. Additionally, it explores the impact of sports psychology and nutrition on athlete well-being and recovery. The review highlights contemporary research trends and practical applications, offering insights for athletes, coaches, and sports scientists.

##plugins.themes.academic_pro.article.details##

How to Cite
Lohia, K. (2024). Sports Science: A Comprehensive Review of Key Concepts and Applications. Innovations in Sports Science, 1(3), 24–27. https://doi.org/10.36676/iss.v1.i3.17

References

  1. Ayyalasomayajula, Madan Mohan Tito. ‘Innovative Water Quality Prediction For Efficient Management Using Ensemble Learning’. Educational Administration: Theory and Practice, vol. 29, no. 4, 2023, pp. 2374–2381.
  2. Amirian, J.; Zhang, B.; Castro, F.V.; Baldelomar, J.J.; Hayet, J.B.; Pettré, J. Opentraj: Assessing prediction complexity in human trajectories datasets. In Proceedings of the Asian Conference on Computer Vision, Kyoto, Japan, 30 November–4 December 2020; pp. 1–17.
  3. Batterham AM and Hopkins WG. Making meaningful inferences about magnitudes. Int J Sports Physiol Perform 1: 50–57, 2006.
  4. Bishop D. An applied research model for the sport sciences. Sports Med 38: 253–263, 2008.
  5. Bishop D, Burnett A, Farrow D, Gabbett T, and Newton RU. Sports-science roundtable: Does sports-science research influence practice. Int J Sports Physiol Perform 1: 161–168, 2006.
  6. Bishop D, Burnett A, Farrow D, Gabbett T, and Newton R. Sports-science roundtable: Does sports-science research influence practice? Int J Sports Physiol Perform 1: 161–168, 2006.
  7. Chen, J.; Li, K.; Bilal, K.; Li, K.; Philip, S.Y. A bi-layered parallel training architecture for large-scale convolutional neural networks. IEEE Trans. Parallel Distrib. Syst. 2018, 30, 965–976.
  8. Farquhar CM, Stryer D, and Slutsky J. Translating research into practice: The future ahead. Int J Qual Health Care 14: 233–249, 2002.
  9. Hoffman JR. Physiological Aspects of Sport Training and Performance. Champaign, IL: Human Kinetics, 2002.
  10. Mohamed, A.; Qian, K.; Elhoseiny, M.; Claudel, C. Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 14424–14432.
  11. Sussman S, Valente TW, Rohrbach LA, Skara S, and Ann Pentz M. Translation in the health professions: Converting science into action. Eval Health Prof 29: 7–32, 2006.
  12. Yu, C.; Ma, X.; Ren, J.; Zhao, H.; Yi, S. Spatio-temporal graph transformer networks for pedestrian trajectory prediction. In Proceedings of the European Conference on Computer Vision, Glasgow, UK, 23–28 August 2020; pp. 507–523.
  13. Wang, C.; Wang, Y.; Xu, M.; Crandall, D.J. Stepwise Goal-Driven Networks for Trajectory Prediction. arXiv 2021, arXiv:abs/2103.14107.

Similar Articles

You may also start an advanced similarity search for this article.