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Surface Electromyography Signal Processing and Classification Techniques

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance...

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Detalles Bibliográficos
Autores principales: Chowdhury, Rubana H., Reaz, Mamun B. I., Ali, Mohd Alauddin Bin Mohd, Bakar, Ashrif A. A., Chellappan, Kalaivani, Chang, Tae. G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3821366/
https://www.ncbi.nlm.nih.gov/pubmed/24048337
http://dx.doi.org/10.3390/s130912431
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author Chowdhury, Rubana H.
Reaz, Mamun B. I.
Ali, Mohd Alauddin Bin Mohd
Bakar, Ashrif A. A.
Chellappan, Kalaivani
Chang, Tae. G.
author_facet Chowdhury, Rubana H.
Reaz, Mamun B. I.
Ali, Mohd Alauddin Bin Mohd
Bakar, Ashrif A. A.
Chellappan, Kalaivani
Chang, Tae. G.
author_sort Chowdhury, Rubana H.
collection PubMed
description Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.
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spelling pubmed-38213662013-11-09 Surface Electromyography Signal Processing and Classification Techniques Chowdhury, Rubana H. Reaz, Mamun B. I. Ali, Mohd Alauddin Bin Mohd Bakar, Ashrif A. A. Chellappan, Kalaivani Chang, Tae. G. Sensors (Basel) Review Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above. MDPI 2013-09-17 /pmc/articles/PMC3821366/ /pubmed/24048337 http://dx.doi.org/10.3390/s130912431 Text en © 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Review
Chowdhury, Rubana H.
Reaz, Mamun B. I.
Ali, Mohd Alauddin Bin Mohd
Bakar, Ashrif A. A.
Chellappan, Kalaivani
Chang, Tae. G.
Surface Electromyography Signal Processing and Classification Techniques
title Surface Electromyography Signal Processing and Classification Techniques
title_full Surface Electromyography Signal Processing and Classification Techniques
title_fullStr Surface Electromyography Signal Processing and Classification Techniques
title_full_unstemmed Surface Electromyography Signal Processing and Classification Techniques
title_short Surface Electromyography Signal Processing and Classification Techniques
title_sort surface electromyography signal processing and classification techniques
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3821366/
https://www.ncbi.nlm.nih.gov/pubmed/24048337
http://dx.doi.org/10.3390/s130912431
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