Analysis of the use of digital technologies in technical training of student skiers

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Gerasimov N.P.
Naberezhnye Chelny Branch of Kazan National Research Technical University named after V.I. A.N. Tupolev-KAI (KNITU-KAI), Naberezhnye Chelny

Petrov R.E.
Yelabuga Institute of Kazan (Volga Region) Federal University, Yelabuga

Khalikov G.Z.
Yelabuga Institute of Kazan (Volga Region) Federal University, Yelabuga

Gerasimova I.G.
Yelabuga Institute of Kazan (Volga Region) Federal University, Yelabuga

Objective of the study was to identify the means and methods of using artificial intelligence in the technical training of student skiers.
Methods and structure of the study. An analysis of research papers was conducted that examined the use of artificial intelligence in the technical training of athletes.
Results and conclusions. The article analyzes the use of artificial intelligence (AI) and the Python programming language to detail the technique of skating skiing through the method of determining key poses and recognizing movements.
By analyzing video and sensor data, coaches can identify errors in technique and offer individual recommendations for each athlete. This allows not only to optimize the training process, but also to minimize the risk of injury, which is especially important for professional skiers. As a result, the implementation of these technologies opens up new horizons in the preparation of athletes and improving their results in competitions.

Keywords: artificial intelligence, programming language, Python, cross-country skiers, detailing, technique, athlete training, skiing, analysis, sports science, recommendations, injuries.

References

  1. Brink, H., Richards, J., & Feverolf, M. (2017). Mashinnoye obucheniye [Machine learning]. Piter.
  2. Vyugin, V. (2017). Matematicheskiye osnovy mashinnogo obucheniya i prognozirovaniya [Mathematical foundations of machine learning and forecasting]. [n.p.].
  3. Domingos, P. (2016). Verkhovnyy algoritm: kak mashinnoye obucheniye izmenit nash mir [The supreme algorithm: how machine learning will change our world]. V. Gorokhov (Trans.), A. Sboev & A. Serenko (Eds.). Mann, Ivanov i Ferber. (Original work published 2015)
  4. Raske, S. (2021). Python i mashinnoye obucheniye [Python and machine learning] (2nd ed.). Dialektika­Vilyams.
  5. Pan, S. (2022). Metod opredeleniya klyuchevykh poz i raspoznavaniya dvizheniy v sporte na osnove glubokogo obucheniya [A Method for determining key pose and recognizing movements in sports based on deep learning]. Mobilnyye informatsionnyye sistemy. https://doi.org/10.1155/2022/5168898
  6. Chardin, B., Massarone, L., & Boschetti, A. (2017). Krupnomasshtabnoye mashinnoye obucheniye vmeste s Python [Large­scale machine learning with Python]. [n.p.].
  7. Wang, J. (2019). Trener na osnove iskusstvennogo intellekta: glubokaya otsenka i analiz pozy cheloveka dlya personalizirovannoy pomoshchi v sportivnykh trenirovkakh [AI­Based coach: deep human pose estimation and analysis for personalized sports training assistance]. In Proceedings of the 27th ACM International Conference on Multimedia (pp. 374–382). https://doi.org/10.1145/3343031.3351062