Classification a sensorimotor task level of complexity for athletes based on physiological indicators using machine learning methods
Authors:
1.
Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies
(Rehabilitation and Sport Psychophysiology Lab, Leading Researcher)
Moscow Center of Advanced Sports Technologies (Sports Psychology Department, physiologist)
Moscow, Russian Federation
Moscow Center of Advanced Sports Technologies (Sports Psychology Department, physiologist)
Moscow, Russian Federation
Type:
Article
eLocation:
Status:
Published
Received:
18.12.2023
Accepted:
22.01.2024
Published:
13.03.2024
Subject area:
VAK Russia 1.5.5
VAK Russia 5.3.3
VAK Russia 1.2.1
VAK Russia 5.8.4
VAK Russia 3.1.33
UDC 159.91
UDC 159.93
UDC 004.93'14
UDC 355.233.22
UDC 796.012
CSCSTI 77.00
CSCSTI 20.00
Russian Classification of Professions by Education 02.00.00
Russian Classification of Professions by Education 06.00.00
Russian Classification of Professions by Education 09.00.00
Russian Classification of Professions by Education 32.00.00
Russian Classification of Professions by Education 44.00.00
Russian Library and Bibliographic Classification 28
Russian Library and Bibliographic Classification 73
Russian Library and Bibliographic Classification 75
Russian Trade and Bibliographic Classification 5
BISAK COM014000 Computer Science
BISAK COM018000 Data Processing
BISAK BIO016000 Sports
BISAK PSY012000 Education & Training
VAK Russia 5.3.3
VAK Russia 1.2.1
VAK Russia 5.8.4
VAK Russia 3.1.33
UDC 159.91
UDC 159.93
UDC 004.93'14
UDC 355.233.22
UDC 796.012
CSCSTI 77.00
CSCSTI 20.00
Russian Classification of Professions by Education 02.00.00
Russian Classification of Professions by Education 06.00.00
Russian Classification of Professions by Education 09.00.00
Russian Classification of Professions by Education 32.00.00
Russian Classification of Professions by Education 44.00.00
Russian Library and Bibliographic Classification 28
Russian Library and Bibliographic Classification 73
Russian Library and Bibliographic Classification 75
Russian Trade and Bibliographic Classification 5
BISAK COM014000 Computer Science
BISAK COM018000 Data Processing
BISAK BIO016000 Sports
BISAK PSY012000 Education & Training
Language:
Russian
Keywords:
task complexity, classifiers, athletes, autonomic indicators, machine learning
Abstract (English):
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%.
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%.
Keywords:
task complexity, classifiers, athletes, autonomic indicators, machine learning
task complexity, classifiers, athletes, autonomic indicators, machine learning
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