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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.

Towards Simplifying Assessment of Athletes Physical Fitness: Evaluation of the Total Physical Performance by Means of Machine Learning