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Authors: Vladimir Kublanov 1 ; Anton Dolganov 1 ; Viktoriya Badtieva 2 and David Akopyan 3

Affiliations: 1 Research Medical and Biological Engineering Centre of High Technologies, Ural Federal University, Mira 19, 620002, Yekaterinburg and Russian Federation ; 2 Moscow Research Center of Medical Rehabilitation and Sports Medicine, Zemlyanoi val 53, 105120, Moscow, Russian Federation, Sechenov University, Trubeczkaya 8, 119991, Moscow and Russian Federation ; 3 Moscow Research Center of Medical Rehabilitation and Sports Medicine, Zemlyanoi val 53, 105120, Moscow and Russian Federation

Keyword(s): Physical Fitness, Machine Learning, Stabilography, Heart Rate Variability, Genetic Programming.

Abstract: The paper describes the methodology for the evaluation of the total physical performance of athletes on the basis of simultaneously recorded signals of stabilography and heart rate variability. An objective assessment of the level of physical performance was carried out using testing on the bicycle ergometer. The use of genetic programming and linear discriminant analysis allowed obtaining the set of diagnostically significant features. The set of diagnostically significant features is able to determine the level of physical fitness using only data from stabilographic studies and heart rate variability with an accuracy of at least 97%. Strength and weaknesses of the proposed approach are discussed.

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Paper citation in several formats:
Kublanov, V.; Dolganov, A.; Badtieva, V. and Akopyan, D. (2019). Towards Simplifying Assessment of Athletes Physical Fitness: Evaluation of the Total Physical Performance by Means of Machine Learning. In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - RAIDERS; ISBN 978-989-758-353-7; ISSN 2184-4305, SciTePress, pages 539-544. DOI: 10.5220/0007699105390544

@conference{raiders19,
author={Vladimir Kublanov. and Anton Dolganov. and Viktoriya Badtieva. and David Akopyan.},
title={Towards Simplifying Assessment of Athletes Physical Fitness: Evaluation of the Total Physical Performance by Means of Machine Learning},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - RAIDERS},
year={2019},
pages={539-544},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007699105390544},
isbn={978-989-758-353-7},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - RAIDERS
TI - Towards Simplifying Assessment of Athletes Physical Fitness: Evaluation of the Total Physical Performance by Means of Machine Learning
SN - 978-989-758-353-7
IS - 2184-4305
AU - Kublanov, V.
AU - Dolganov, A.
AU - Badtieva, V.
AU - Akopyan, D.
PY - 2019
SP - 539
EP - 544
DO - 10.5220/0007699105390544
PB - SciTePress

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