Control Systems and Computers

https://doi.org/10.15407/csc.2022.02.064

Control Systems and Computers, 2022, Issue 2 (298), pp. 64-69

UDK 681.513;  796.015

Savchenko-Syniakova Yevheniya A., PhD (Eng.), Senior Research Associate, International Research and Training Center for Information Technologies and Systems NAS and MES of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine, ORCID: https://orcid.org/0000-0003-4851-9664, savchenko_e@meta.ua

Savchenko Costantine Yu., Master, National University of Ukraine on Physical Education and Sport, Kyiv, str. Fizkultury, 1, ORCID: https://orcid.org/0000-0003-3552-2717, constantine.savchenko@gmail.com

Mathematical Modeling of the Optimal Training Load in the Sailing

Introduction. Sailing is a sport that places high demands on the physical fitness of an athlete. Despite the fact that this sport has been practiced for a very long time, experts have not paid due attention to assessing the impact of the athlete’s load during training on his physical fatigue. The use of mathematical modeling methods at the stage of training athletes to select the optimal load will make training more effective. In order to determine the modeling method, which will allow quite simply and easily to find the dependence of the optimal physical load on the physiological indicators of athletes, a review of the most popular modeling methods in sports was carried out.

The purpose of this article is to study the problem of modeling the optimal training load in sailing in order to find an effective method for building models of the dependence of the athlete’s fatigue indicators on the indicators characterizing the athlete’s condition.

Results. A number of indicators characterizing the choice of optimal physical activity in sailing are given. An approach to the construction of a mathematical model for choosing the optimal physical load for yachtsmen during training is proposed.

Conclusions. The article explores approaches to modeling the processes that occur during the selection of a training load in sports. Based on a review of existing methods, an inductive approach was chosen to build models for choosing the optimal load in sailing. It is planned that with the help of this approach, models of the dependence of indicators characterizing physical activity on indicators characterizing the state of athletes will be obtained, which will be given in subsequent works.

 Download full text! (On Ukrainian)

Keywords: mathematical model, modeling, optimal training load, sailing, inductive modeling, GMDH.

1. Bateup, B., Compton, H., Withers, S., Duthie, G.M. (2016). The influence of training load on markers of fatigue in junior male semi-elite sailors. Australian Strength and Conditioning Association. At: Melbourne, Australia. DOI: 10.13140/RG.2.2.30398.48966.
2. Bojsen-Moller, J., Larsson, B., Aagaard, P. (2015). Physical requirements in Olympic sailing. European Journal of Sport Science, 15(3), 220-227.
   https://doi.org/10.1080/17461391.2014.955130
3. Vangelakoudi, A., Vogiatzis, I., Geladas, N. (2007). Anaerobic capacity, isometric endurance, and Laser sailing performance. Journal of Sports Sciences, 25(10), 1095-1100.
   https://doi.org/10.1080/02640410601165288
4. Rajsp, A., Fister, I. (2020). A systematic literature review of intelligent data analysis methods for smart sport training. Applied Sciences, 10(9), 3013.
   https://doi.org/10.3390/app10093013
5. Matveev, L.P.; Zdornyj, A.P. (1981). Determination of the Notion: “Training an Athlete” and “Sports Training”; Progress: St. Columbus, OH, USA, pp. 21-25.
6. Fister, I.; Fister, I., Jr.; Fister, D. (2019). Computational Intelligence in Sports. Springer: Cham, Switzerland.
   https://doi.org/10.1007/978-3-030-03490-0
7. Kostyukevych, V. M., Shynkaruk, O. A., Voronova, V. I., Borysova, O. V., Kostyukevych, V. M., Shynkaruk, O. A. et all. (2017). Metody kontrolyu za trenuvalʹnymy i zmahalʹnymy navantazhennyamy. Kyyiv: KNT. https://www.researchgate.net/publication/323365800.
8. Lysenko, Ye.N., Mishchenko, V.S. (2016). Reaktivnyye svoystva kardiorespiratornoy sistemy v protsesse napryazhennoy fizicheskoy nagruzki i posle neye. Sportivnaya meditsina. No 1, pp. 11-19. (In Russian).
9. Lacour, J. R., Messonnier, L., Bourdin, M. (2007). The leveling-off of oxygen uptake is related to blood lactate accumulation. Retrospective study of 94 elite rowers. European Journal of Applied Physiology. 101. pp. 241- 247. DOI: 1007/s00421-007-0487-7.
   https://doi.org/10.1007/s00421-007-0487-7
10. McKay B.R., Paterson D.H., Kowalchuk J.M. Effect of short-term high-intensity interval training vs. continuous training on O2 uptake kinetics, muscle deoxygenation, and exercise performance. Journal of Applied Physiology. 107, 2009, pp. 128-138. https://doi.org/10.1152/japplphysiol.90828.2008.
    https://doi.org/10.1152/japplphysiol.90828.2008
11. D’yachenko A.Yu. (2004). Sovershenstvovaniye spetsial’noy vynoslivosti kvalifitsirovannykh sportsmenov v akademicheskoy greble. Kyiv: NPF “Slavutich-Del’fin”. 338 p.
12. Wang, V, Mayer, F, Bonaventura, K et al. (2017). Intrinsic And Extrinsic Injury Risk Factors Of Elite Winter Sports Athlete In Training. British Journal of Sports Medicine. 51, 406 p. http://dx.doi.org/10.1136/bjsports-2016-097372.309.
    https://doi.org/10.1136/bjsports-2016-097372.309
13. Castagna, O., Brisswalter, J. (2007). Assessment of energy demand in Laser sailing: influences of exercise duration and performance level. European Journal of Applied Physiology, 99 (2), pp. 95-101.
    https://doi.org/10.1007/s00421-006-0336-0
14. Sportyvna fiziolohiya u skhemakh i tablytsyakh: posibnyk dlya studentiv instytutiv fizychnoyi kulʹtury. (2013). / Yezhova O.O. Sumy: Sum DPU im. A. S. Makarenka, 164 p.
15. Sportyvna fiziolohiya. Navch.-metodychnyy posib. Lʹviv: SPOLOM, 2006. 160 p.
16. Zbirnyk lektsiy z dystsypliny “Fiziolohichni osnovy fizychnoho vykhovannya i sportu” dlya pidhotovky bakalavriv spetsialʹnosti 014.11 Serednya osvita “Fizychna kulʹtura”. (2018). / Prokopenko Yu.S.; Kremenchutsʹkyy pedahohichnyy koledzh imeni A.S. Makarenka. Kremenchuk. 74 p.
17. Imbach, F., Perrey, S., Chailan, R., Meline, T., Candau, R. (2020). Training load responses modelling in elite sports: how to deal with generalisation?. DOI: https://doi.org/10.21203/rs.3.rs-128940/v1.
    https://doi.org/10.21203/rs.3.rs-128940/v1
18. Pandelo, D. (2019). Establishment of an Optimal Training Load in Multisport. https://www.researchgate.net/publication/335313361.
19. Me, E., Unold, O. (2011). Machine learning approach to model sport training. Computers in human behavior, 27(5), pp. 1499-1506.
    https://doi.org/10.1016/j.chb.2010.10.014
20. Fister Jr, I., Ljubič, K., Suganthan, P. N., Perc, M., & Fister, I. (2015). Computational intelligence in sports: challenges and opportunities within a new research domain. Applied Mathematics and Computation, 262, pp. 178-186. https://doi.org/10.1016/j.amc.2015.04.004
    https://doi.org/10.1016/j.amc.2015.04.004
21. Beal, R., Norman, T. J., & Ramchurn, S. D. (2019). Artificial intelligence for team sports: a survey. The Knowledge Engineering Review, 34.
    https://doi.org/10.1017/S0269888919000225
22. Araujo, D., Couceiro, M., Seifert, L., Sarmento, H., Davids, K. (2021). Artificial Intelligence in Sport Performance Analysis (1st ed.). Routledge. https://doi.org/10.4324/9781003163589.
    https://doi.org/10.4324/9781003163589
23. Hamlin, M. J., Wilkes, D., Elliot, C. A., Lizamore, C. A., Kathiravel, Y. (2019). Monitoring training loads and perceived stress in young elite university athletes. Frontiers in physiology, 10, p. 34.
    https://doi.org/10.3389/fphys.2019.00034
24. Ivakhnenko, A.G., 1968. “Group method of data handling as competitor for the method of stochastic approximation”, Soviet Automatic Control, n3, pp. 58-72 (In Ukrainian).
25. Ivakhnenko, A.G., Stepashko, V.S., 1985. Noise-immunity of modeling. Kiev: Naukova dumka, 216 p. (In Russian).
26. Madala H.R., Ivakhnenko A.G. Inductive learning algorithms for complex systems modeling. New York: Boca Raton, CRC Press, 1994. 384 p.

Received 23.08.2022