ARTIFICIAL INTELLIGENCE MODELING OF PHYSIOLOGICAL PARAMETERS AT ANAEROBIC THRESHOLD

DOI: https://doi.org/10.14529/hsm22s206
Keywords: anaerobic threshold, machine learning, incremental exercise, model

Abstract

Aim. The paper aims to develop a predictive model of quantitative physiological parameters at anaerobic threshold during the exercise test to fatigue by means of artificial intelligence. Materials and methods. The study involved 1273 athletes aged from 18 to 35 years. All athletes performed incremental exercise to fatigue. Physiological parameters at anaerobic threshold were obtained with machine learning algorithms from the Scikit-learn library: linear regression, regression random forest, gradient boosting regression, support vector regression. Results. The support vector regression algorithm achieves the best results for heart rate, minute respiratory volume, and O2/HR (r2 is 0.82, 0.90, and 0.91, respectively). In terms of VO2, VCO2, the support vector regression algorithm is inferior in prediction accuracy to the linear regression algorithm (r2 is 0.87, 0.86, respectively) by –0.76% and –0.16%, respectively. Conclusion. The quantitative accuracy of the model based on the support vector regression algorithm is comparable with the results of similar studies. Thus, it allows us to use it as a recommendation system for measuring quantitative parameters at anaerobic threshold. The weak point of the model is the use of protocol-dependent load attributes, so further improvement of the model will be aimed at correcting this shortcoming.

Author Biographies

A. Chikov , Research Institute of Hygiene, Occupational Pathology and Human Ecology, Saint Petersburg, Russia

Candidate of Biological Sciences, Associate Professor, Laboratory Head, Research Institute of Hygiene, Occupational Pathology and Human Ecology, Saint Petersburg, Russia.

E. Pavlov , Peter the Great Saint Petersburg Polytechnic University, Saint Petersburg, Russia

Master’s Degree Student, Higher School of Software Engineering, Peter the Great Saint Petersburg Polytechnic University, Saint Petersburg, Russia.

N. Egorov , Research Institute of Hygiene, Occupational Pathology and Human Ecology, Saint Petersburg, Russia

Senior Researcher, Research Institute of Hygiene, Occupational Pathology and Human Ecology, Saint Petersburg, Russia.

D. Medvedev , Research Institute of Hygiene, Occupational Pathology and Human Ecology, Saint Petersburg, Russia; North-Western State Medical University named after I.I. Mechnikov, Saint Petersburg, Russia

Doctor of Medical Sciences, Professor, Head of the Department of Physiological Assessment and Medical Correction, Research Institute of Hygiene, Occupational Pathology and Human Ecology, Saint Petersburg, Russia; Professor of the Department of Exercise Therapy and Sports Medicine, North-Western State Medical University named after I.I. Mechnikov, Saint Petersburg, Russia.

S. Chikova , Research Institute of Hygiene, Occupational Pathology and Human Ecology, Saint Petersburg, Russia

Candidate of Biological Sciences, Associate Professor, Laboratory Head, Research Institute of Hygiene, Occupational Pathology and Human Ecology, Saint Petersburg, Russia.

P. Drobintsev , Peter the Great Saint Petersburg Polytechnic University, Saint Petersburg, Russia

Candidate of Technical Sciences, Associate Professor, Director of the Higher School of Software Engineering, Peter the Great Saint Petersburg Polytechnic University, Saint Petersburg, Russia.

References

References on translit

Published
2022-12-29
How to Cite
Chikov, A., Pavlov, E., Egorov, N., Medvedev, D., Chikova, S., & Drobintsev, P. (2022). ARTIFICIAL INTELLIGENCE MODELING OF PHYSIOLOGICAL PARAMETERS AT ANAEROBIC THRESHOLD. Human. Sport. Medicine, 22(S2), 46-53. https://doi.org/10.14529/hsm22s206
Issue
Vol 22 No S2 (2022)
Section
Physiology

Copyright (c) 2022 Human. Sport. Medicine

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