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CLASSIFICATION A SENSORIMOTOR TASK LEVEL OF COMPLEXITY FOR ATHLETES BASED ON PHYSIOLOGICAL INDICATORS USING MACHINE LEARNING METHODS Classification a sensorimotor task level of complexity for athletes based on physiological indicators using machine learning methods

Published in Russian Journal of Information Technology in Sports · Volume 1, Issue 1 · Pages 17–20 · Rubric: DIGITAL TECHNOLOGIES IN EXTREME AND SPORTS PHYSIOLOGY
DOI: https://doi.org/10.62105/2949-6349-2024-1-1-17-20 · EDN: FTFHIY
Received: 18.12.2023 Accepted: 22.01.2024 Published: 13.03.2024 Language of publication: RUS
The study aimed to identify the most sensitive autonomic indicators reflecting the level of complexity of the sensorimotor task performed by athletes using various machine learning methods (classification algorithms). As tasks of two levels of difficulty, we used the audio-motor synchronization task: to tap in synchrony with a metronome rhythmic sound (a simple task) and to tap the same rhythm without auditory cues (rhythm memory task, a complex task). Heart rate, respiratory parameters, skin conduction, and EEG were recorded. The most accurate classification was demonstrated by the Classification and Regression Trees (C&RT) model – the error was 18.3%.
task complexity, classifiers, athletes, autonomic indicators, machine learning
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