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Heart Rate Variability-Based Subjective Physical Fatigue Assessment

Accurate assessment of physical fatigue is crucial to preventing physical injury caused by excessive exercise, overtraining during daily exercise and professional sports training. However, as a subjective feeling of an individual, physical fatigue is difficult for others to objectively evaluate. Hea...

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Detalles Bibliográficos
Autores principales: Ni, Zhiqiang, Sun, Fangmin, Li, Ye
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9100264/
https://www.ncbi.nlm.nih.gov/pubmed/35590889
http://dx.doi.org/10.3390/s22093199
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author Ni, Zhiqiang
Sun, Fangmin
Li, Ye
author_facet Ni, Zhiqiang
Sun, Fangmin
Li, Ye
author_sort Ni, Zhiqiang
collection PubMed
description Accurate assessment of physical fatigue is crucial to preventing physical injury caused by excessive exercise, overtraining during daily exercise and professional sports training. However, as a subjective feeling of an individual, physical fatigue is difficult for others to objectively evaluate. Heart rate variability (HRV), which is derived from electrocardiograms (ECG) and controlled by the autonomic nervous system, has been demonstrated to be a promising indicator for physical fatigue estimation. In this paper, we propose a novel method for the automatic and objective classification of physical fatigue based on HRV. First, a total of 24 HRV features were calculated. Then, a feature selection method was proposed to remove useless features that have a low correlation with physical fatigue and redundant features that have a high correlation with the selected features. After feature selection, the best 11 features were selected and were finally used for physical fatigue classifying. Four machine learning algorithms were trained to classify fatigue using the selected features. The experimental results indicate that the model trained using the selected 11 features could classify physical fatigue with high accuracy. More importantly, these selected features could provide important information regarding the identification of physical fatigue.
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spelling pubmed-91002642022-05-14 Heart Rate Variability-Based Subjective Physical Fatigue Assessment Ni, Zhiqiang Sun, Fangmin Li, Ye Sensors (Basel) Article Accurate assessment of physical fatigue is crucial to preventing physical injury caused by excessive exercise, overtraining during daily exercise and professional sports training. However, as a subjective feeling of an individual, physical fatigue is difficult for others to objectively evaluate. Heart rate variability (HRV), which is derived from electrocardiograms (ECG) and controlled by the autonomic nervous system, has been demonstrated to be a promising indicator for physical fatigue estimation. In this paper, we propose a novel method for the automatic and objective classification of physical fatigue based on HRV. First, a total of 24 HRV features were calculated. Then, a feature selection method was proposed to remove useless features that have a low correlation with physical fatigue and redundant features that have a high correlation with the selected features. After feature selection, the best 11 features were selected and were finally used for physical fatigue classifying. Four machine learning algorithms were trained to classify fatigue using the selected features. The experimental results indicate that the model trained using the selected 11 features could classify physical fatigue with high accuracy. More importantly, these selected features could provide important information regarding the identification of physical fatigue. MDPI 2022-04-21 /pmc/articles/PMC9100264/ /pubmed/35590889 http://dx.doi.org/10.3390/s22093199 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ni, Zhiqiang
Sun, Fangmin
Li, Ye
Heart Rate Variability-Based Subjective Physical Fatigue Assessment
title Heart Rate Variability-Based Subjective Physical Fatigue Assessment
title_full Heart Rate Variability-Based Subjective Physical Fatigue Assessment
title_fullStr Heart Rate Variability-Based Subjective Physical Fatigue Assessment
title_full_unstemmed Heart Rate Variability-Based Subjective Physical Fatigue Assessment
title_short Heart Rate Variability-Based Subjective Physical Fatigue Assessment
title_sort heart rate variability-based subjective physical fatigue assessment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9100264/
https://www.ncbi.nlm.nih.gov/pubmed/35590889
http://dx.doi.org/10.3390/s22093199
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