Comparison of Endurance Time Prediction of Biceps Brachii Using Logarithmic Parameters of a Surface Electromyogram during Low-Moderate Level Isotonic Contractions

At relatively low effort level tasks, surface electromyogram (sEMG) spectral parameters have demonstrated an inconsistent ability to monitor localized muscle fatigue and predict endurance capacity. The main purpose of this study was to assess the potential of the endurance time (T<sub>end</...

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Main Authors: Chang-ok Cho, Jin-Hyoung Jeong, Yun-jeong Kim, Jee Hun Jang, Sang-Sik Lee, Ki-young Lee
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/11/6/2861
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author Chang-ok Cho
Jin-Hyoung Jeong
Yun-jeong Kim
Jee Hun Jang
Sang-Sik Lee
Ki-young Lee
author_facet Chang-ok Cho
Jin-Hyoung Jeong
Yun-jeong Kim
Jee Hun Jang
Sang-Sik Lee
Ki-young Lee
author_sort Chang-ok Cho
collection DOAJ
description At relatively low effort level tasks, surface electromyogram (sEMG) spectral parameters have demonstrated an inconsistent ability to monitor localized muscle fatigue and predict endurance capacity. The main purpose of this study was to assess the potential of the endurance time (T<sub>end</sub>) prediction using logarithmic parameters compared to raw data. Ten healthy subjects performed five sets of voluntary isotonic contractions until their exhaustion at 20% of their maximum voluntary contraction (MVC) level. We extracted five sEMG spectral parameters namely the power in the low frequency band (LFB), the mean power frequency (MPF), the high-to-low ratio between two frequency bands (H/L-FB), the Dimitrov spectral index (DSI), and the high-to-low ratio between two spectral moments (H/L-SM), and then converted them to logarithms. Changes in these ten parameters were monitored using area ratio and linear regressive slope as statistical predictors and estimating from onset at every 10% of T<sub>end</sub>. Significant correlations (r > 0.5) were found between log(T<sub>end</sub>) and the linear regressive slopes in the logarithmic H/L-SM at every 10% of T<sub>end</sub>. In conclusion, logarithmic parameters can be used to describe changes in the fatigue content of sEMG and can be employed as a better predictor of T<sub>end</sub> in comparison to the raw parameters.
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spelling doaj.art-548876cb970c4e878e45255028c627702023-11-21T11:38:55ZengMDPI AGApplied Sciences2076-34172021-03-01116286110.3390/app11062861Comparison of Endurance Time Prediction of Biceps Brachii Using Logarithmic Parameters of a Surface Electromyogram during Low-Moderate Level Isotonic ContractionsChang-ok Cho0Jin-Hyoung Jeong1Yun-jeong Kim2Jee Hun Jang3Sang-Sik Lee4Ki-young Lee5Korea Paralympic Committee, Seoul 05540, KoreaDepartment of Biomedical IT, Catholic Kwandong University, Gangneung-si 25601, KoreaDepartment of Sport and Leisure Studies, Catholic Kwandong University, Gangneung-si 25601, KoreaDepartment of Sport and Leisure Studies, Catholic Kwandong University, Gangneung-si 25601, KoreaDepartment of Biomedical Engineering, Catholic Kwandong University, Gangneung-si 25601, KoreaDepartment of Biomedical Engineering, Catholic Kwandong University, Gangneung-si 25601, KoreaAt relatively low effort level tasks, surface electromyogram (sEMG) spectral parameters have demonstrated an inconsistent ability to monitor localized muscle fatigue and predict endurance capacity. The main purpose of this study was to assess the potential of the endurance time (T<sub>end</sub>) prediction using logarithmic parameters compared to raw data. Ten healthy subjects performed five sets of voluntary isotonic contractions until their exhaustion at 20% of their maximum voluntary contraction (MVC) level. We extracted five sEMG spectral parameters namely the power in the low frequency band (LFB), the mean power frequency (MPF), the high-to-low ratio between two frequency bands (H/L-FB), the Dimitrov spectral index (DSI), and the high-to-low ratio between two spectral moments (H/L-SM), and then converted them to logarithms. Changes in these ten parameters were monitored using area ratio and linear regressive slope as statistical predictors and estimating from onset at every 10% of T<sub>end</sub>. Significant correlations (r > 0.5) were found between log(T<sub>end</sub>) and the linear regressive slopes in the logarithmic H/L-SM at every 10% of T<sub>end</sub>. In conclusion, logarithmic parameters can be used to describe changes in the fatigue content of sEMG and can be employed as a better predictor of T<sub>end</sub> in comparison to the raw parameters.https://www.mdpi.com/2076-3417/11/6/2861electromyographymuscleendurance capacityisotonicprediction capability
spellingShingle Chang-ok Cho
Jin-Hyoung Jeong
Yun-jeong Kim
Jee Hun Jang
Sang-Sik Lee
Ki-young Lee
Comparison of Endurance Time Prediction of Biceps Brachii Using Logarithmic Parameters of a Surface Electromyogram during Low-Moderate Level Isotonic Contractions
Applied Sciences
electromyography
muscle
endurance capacity
isotonic
prediction capability
title Comparison of Endurance Time Prediction of Biceps Brachii Using Logarithmic Parameters of a Surface Electromyogram during Low-Moderate Level Isotonic Contractions
title_full Comparison of Endurance Time Prediction of Biceps Brachii Using Logarithmic Parameters of a Surface Electromyogram during Low-Moderate Level Isotonic Contractions
title_fullStr Comparison of Endurance Time Prediction of Biceps Brachii Using Logarithmic Parameters of a Surface Electromyogram during Low-Moderate Level Isotonic Contractions
title_full_unstemmed Comparison of Endurance Time Prediction of Biceps Brachii Using Logarithmic Parameters of a Surface Electromyogram during Low-Moderate Level Isotonic Contractions
title_short Comparison of Endurance Time Prediction of Biceps Brachii Using Logarithmic Parameters of a Surface Electromyogram during Low-Moderate Level Isotonic Contractions
title_sort comparison of endurance time prediction of biceps brachii using logarithmic parameters of a surface electromyogram during low moderate level isotonic contractions
topic electromyography
muscle
endurance capacity
isotonic
prediction capability
url https://www.mdpi.com/2076-3417/11/6/2861
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