Application of Surface Electromyography in Exercise Fatigue: A Review

Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyogr...
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Autor*in:	Jiaqi Sun [verfasserIn] Guangda Liu [verfasserIn] Yubing Sun [verfasserIn] Kai Lin [verfasserIn] Zijian Zhou [verfasserIn] Jing Cai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	exercise fatigue sEMG machine learning feature extraction classification

Übergeordnetes Werk:	In: Frontiers in Systems Neuroscience - Frontiers Media S.A., 2008, 16(2022)
Übergeordnetes Werk:	volume:16 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fnsys.2022.893275

Katalog-ID:	DOAJ079353169

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container_title	Frontiers in Systems Neuroscience
authorswithroles_txt_mv	Jiaqi Sun @@aut@@ Guangda Liu @@aut@@ Yubing Sun @@aut@@ Kai Lin @@aut@@ Zijian Zhou @@aut@@ Jing Cai @@aut@@
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callnumber-first	R - Medicine
author	Jiaqi Sun
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topic_title	RC321-571 Application of Surface Electromyography in Exercise Fatigue: A Review exercise fatigue sEMG machine learning feature extraction classification
topic	misc RC321-571 misc exercise fatigue misc sEMG misc machine learning misc feature extraction misc classification misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc exercise fatigue misc sEMG misc machine learning misc feature extraction misc classification misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	Application of Surface Electromyography in Exercise Fatigue: A Review
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title_full	Application of Surface Electromyography in Exercise Fatigue: A Review
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title_sort	application of surface electromyography in exercise fatigue: a review
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title_auth	Application of Surface Electromyography in Exercise Fatigue: A Review
abstract	Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.
abstractGer	Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.
abstract_unstemmed	Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.
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title_short	Application of Surface Electromyography in Exercise Fatigue: A Review
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