ENERGY SUPPLY OF GAME ACTIVITY OF THE STUDENTS SPECIALIZING IN VOLLEYBALL
DOI:
https://doi.org/10.32782/Keywords:
biopedagogics, educational process, volleyball, students, artificial intellect, physical state of the bodyAbstract
Modeling of the body physical state of the students belonging to various groups of sports and pedagogical improvement (SPI), characterized by the purpose, biomechanical parameters of movements, the nature of muscle contractions, the power and duration of work, energy supply mechanisms in accordance with the peculiarities of the sport using intelligent data analysis allows to detail the ratio of means and methods, which are used in the pedagogical process. The objective of the study is to clarify the structure and conformity of the functional state of the body systems of students specializing in volleyball, with the dominance of energy supply regimes for the activities performance. Organization. The research involved 27 male participants aged 19–21 who attended the SPI volleyball section and are part of the student first team of the «Burevestnik » sports club, which functions at the facilities of the Physical Education Department of the "Chernigov Collegium" National University named after T. G. Shevchenko. Results. According to the results of the studies, informative features were identified, which, with high probability, differentiate students volleyball players according to the playing roles, as well as by the dominance of attacking or defensive actions during their playing activity. Players who are dominated by the speed-strength component of the activity (attack line) had relatively low absolute values of cardiac rate power and the total power of the HRV spectrum. Whereas the players with the dominance of the aerobic component (defense line) had high absolute values of HRV powers, accompanied by a significant insufficient restoration of absolute values of spectral powers after the performance of dose-related physical loads of a cyclic character. For players with a hypersthenic type of constitution, in which the anaerobic speed-strength component of the activity is dominant, the presence of an oxygen deficiency while carrying out cyclic aerobic loads is characteristic, smaller values of the spectral powers of the HRV in the restitution phase after performing the PWC 170 test could be observed. Players with the dominance of the aerobic component of activity realization display greater variability of the heart rate with less centralization of HRV regulation. Players with dominance of parasympathetic regulation of HRV have relatively high absolute power values of all spectra, both in basal conditions and during restitution.
References
1. Белоцерковский З. Б. Эргометрические и кардиологические критерии физической работоспособности у спортсменов / З. Б. Белоцерковский. – Москва : Советский спорт, 2005. – 312 с.
2. Иваницкий М. Ф. Анатомия человека / М. Ф. Иваницкий. – изд. 7-е. – Москва : Олимпия, 2008. – 624 с.
3. Михайлов В. М. Вариабельность ритма сердца: опыт практического применения метода / В. М. Михайлов. – Иваново : Ивановская гос. мед. акад., 2002. – 290 с.
4. Мюллер А. Введение в машинное обучение с помощью Python: руководство для специалистов по работе с данными / Андреас Мюллер, Сара Гвидо. – Москва ; Санкт-Петербург : Диалектика, Альфа-Книга, 2017. – 472 с.
5. Приймак С. Г. Спортивно-педагогічне удосконалення студентів: морфофункціональ-не забезпечення діяльності : [монографія] / С. Г. Приймак. – Чернігів : Десна, 2018. – 292 с.
6. Романенко В. А. Диагностика двигательных способностей : учеб. пособие. для студ. биол. ф-тов. – Донецк : ДонНУ, 2005. – 290 с.
7. Aftalion A. Optimization of running strategies based on anaerobic energy and variations of velocity / Aftalion A., Bonnans J. F. // SIAM J. Applied Math. – 2014. – Vol. 74, N 5. – P. 1615–1636.
8. Churchill T. Modelling athletic training and performance: a hybrid artificial neural network ensemble approach: PhD Thesis / Churchill T.; University of Canberra, 2014–238 р.
9. Heart rate modelling as a potential physical fitness assessment for runners and cyclists. Proceedings of the Machine Learning and Data Mining for Sports Analytics Workshop at ECML/PPKD / Dimitri de Smet, Marc Francaux, Julien M. Hendrickx and Michel Verleysen. – Riva del Garda, Italy, 2016.
10. Gilbert Owusu G. AI and computer-based methods in performance evaluation of sporting feats: an overview / Gilbert Owusu G. // Artifcial Intelligence Review. – 2007. – Vol. 27, N 1. – P. 57–70.
11. Machine Learning and Data Mining for Sports Analytics ECML / PKDD 4th workshop. – Skopje, Macedonia, 2017.
12. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability. Standarts of Measurement. Physiological interpretation and clinical use // Circulation. – 1996. – Vol. 93. – P. 1043–1065.
13. Wicker P. Analysis of problems using Data Mining techniques – fndings from sports clubs in Germany / Wicker P., Breuer C. // European Journal for Sport and Society. – 2010, Vol. 7, N 2. – P. 131–140.
