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Proposed fatigue index for the objective detection of muscle fatigue using surface electromyography and a double-step binary classifier

The objective detection of muscle fatigue reports the moment at which a muscle fails to sustain the required force. Such a detection prevents any further injury to the muscle following fatigue. However, the objective detection of muscle fatigue still requires further investigation. This paper presen...

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Main Authors: Qassim, Hassan M., Hasan, Wan Zuha Wan, Ramli, Hafiz R., Harith, Hazreen Haizi, Inche Mat, Liyana Najwa, Ismail, Luthffi Idzhar
Format: Article
Published: MDPI 2022
Online Access:http://psasir.upm.edu.my/id/eprint/102930/
https://www.mdpi.com/1424-8220/22/5/1900
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spelling my.upm.eprints.1029302024-06-30T23:29:44Z http://psasir.upm.edu.my/id/eprint/102930/ Proposed fatigue index for the objective detection of muscle fatigue using surface electromyography and a double-step binary classifier Qassim, Hassan M. Hasan, Wan Zuha Wan Ramli, Hafiz R. Harith, Hazreen Haizi Inche Mat, Liyana Najwa Ismail, Luthffi Idzhar The objective detection of muscle fatigue reports the moment at which a muscle fails to sustain the required force. Such a detection prevents any further injury to the muscle following fatigue. However, the objective detection of muscle fatigue still requires further investigation. This paper presents an algorithm that employs a new fatigue index for the objective detection of muscle fatigue using a double-step binary classifier. The proposed algorithm involves analyzing the acquired sEMG signals in both the time and frequency domains in a double-step investigation. The first step involves calculating the value of the integrated EMG (IEMG) to determine the continuous contraction of the muscle being investigated. It was found that the IEMG value continued to increase with prolonged muscle contraction and progressive fatigue. The second step involves differentiating between the high-frequency components (HFC) and low-frequency components (LFC) of the EMG, and calculating the fatigue index. Basically, the segmented EMG signal was filtered by two band-pass filters separately to produce two sub-signals, namely, a high-frequency sub-signal (HFSS) and a low-frequency sub-signal (LFSS). Then, the instantaneous mean amplitude (IMA) was calculated for the two sub-signals. The proposed algorithm indicates that the IMA of the HFSS tends to decrease during muscle fatigue, while the IMA of the LFSS tends to increase. The fatigue index represents the difference between the IMA values of the LFSS and HFSS, respectively. Muscle fatigue was found to be present and was objectively detected when the value of the proposed fatigue index was equal to or greater than zero. The proposed algorithm was tested on 75 EMG signals that were extracted from 75 middle deltoid muscles. The results show that the proposed algorithm had an accuracy of 94.66% in distinguishing between conditions of muscle fatigue and non-fatigue. MDPI 2022 Article PeerReviewed Qassim, Hassan M. and Hasan, Wan Zuha Wan and Ramli, Hafiz R. and Harith, Hazreen Haizi and Inche Mat, Liyana Najwa and Ismail, Luthffi Idzhar (2022) Proposed fatigue index for the objective detection of muscle fatigue using surface electromyography and a double-step binary classifier. Sensors, 22 (5). art. no. 1900. pp. 1-24. ISSN 1424-8220 https://www.mdpi.com/1424-8220/22/5/1900 10.3390/s22051900
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description The objective detection of muscle fatigue reports the moment at which a muscle fails to sustain the required force. Such a detection prevents any further injury to the muscle following fatigue. However, the objective detection of muscle fatigue still requires further investigation. This paper presents an algorithm that employs a new fatigue index for the objective detection of muscle fatigue using a double-step binary classifier. The proposed algorithm involves analyzing the acquired sEMG signals in both the time and frequency domains in a double-step investigation. The first step involves calculating the value of the integrated EMG (IEMG) to determine the continuous contraction of the muscle being investigated. It was found that the IEMG value continued to increase with prolonged muscle contraction and progressive fatigue. The second step involves differentiating between the high-frequency components (HFC) and low-frequency components (LFC) of the EMG, and calculating the fatigue index. Basically, the segmented EMG signal was filtered by two band-pass filters separately to produce two sub-signals, namely, a high-frequency sub-signal (HFSS) and a low-frequency sub-signal (LFSS). Then, the instantaneous mean amplitude (IMA) was calculated for the two sub-signals. The proposed algorithm indicates that the IMA of the HFSS tends to decrease during muscle fatigue, while the IMA of the LFSS tends to increase. The fatigue index represents the difference between the IMA values of the LFSS and HFSS, respectively. Muscle fatigue was found to be present and was objectively detected when the value of the proposed fatigue index was equal to or greater than zero. The proposed algorithm was tested on 75 EMG signals that were extracted from 75 middle deltoid muscles. The results show that the proposed algorithm had an accuracy of 94.66% in distinguishing between conditions of muscle fatigue and non-fatigue.
format Article
author Qassim, Hassan M.
Hasan, Wan Zuha Wan
Ramli, Hafiz R.
Harith, Hazreen Haizi
Inche Mat, Liyana Najwa
Ismail, Luthffi Idzhar
spellingShingle Qassim, Hassan M.
Hasan, Wan Zuha Wan
Ramli, Hafiz R.
Harith, Hazreen Haizi
Inche Mat, Liyana Najwa
Ismail, Luthffi Idzhar
Proposed fatigue index for the objective detection of muscle fatigue using surface electromyography and a double-step binary classifier
author_facet Qassim, Hassan M.
Hasan, Wan Zuha Wan
Ramli, Hafiz R.
Harith, Hazreen Haizi
Inche Mat, Liyana Najwa
Ismail, Luthffi Idzhar
author_sort Qassim, Hassan M.
title Proposed fatigue index for the objective detection of muscle fatigue using surface electromyography and a double-step binary classifier
title_short Proposed fatigue index for the objective detection of muscle fatigue using surface electromyography and a double-step binary classifier
title_full Proposed fatigue index for the objective detection of muscle fatigue using surface electromyography and a double-step binary classifier
title_fullStr Proposed fatigue index for the objective detection of muscle fatigue using surface electromyography and a double-step binary classifier
title_full_unstemmed Proposed fatigue index for the objective detection of muscle fatigue using surface electromyography and a double-step binary classifier
title_sort proposed fatigue index for the objective detection of muscle fatigue using surface electromyography and a double-step binary classifier
publisher MDPI
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/102930/
https://www.mdpi.com/1424-8220/22/5/1900
_version_ 1804067027035357184
score 13.211869

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