Surface Electromyography Signal Processing and Classification Techniques
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chowdhury, Rubana H. | - |
dc.contributor.author | Reaz, Mamun B. I. | - |
dc.contributor.author | Ali, Mohd Alauddin Bin Mohd | - |
dc.contributor.author | Bakar, Ashrif A. A. | - |
dc.contributor.author | Chellappan, Kalaivani | - |
dc.contributor.author | Chang, Tae. G. | - |
dc.date.available | 2019-03-09T01:38:18Z | - |
dc.date.issued | 2013-09 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.issn | 1424-3210 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/14361 | - |
dc.description.abstract | 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. | - |
dc.format.extent | 36 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI AG | - |
dc.title | Surface Electromyography Signal Processing and Classification Techniques | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/s130912431 | - |
dc.identifier.bibliographicCitation | SENSORS, v.13, no.9, pp 12431 - 12466 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000328625800069 | - |
dc.identifier.scopusid | 2-s2.0-84884358843 | - |
dc.citation.endPage | 12466 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 12431 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 13 | - |
dc.type.docType | Review | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | electromyography | - |
dc.subject.keywordAuthor | noise source | - |
dc.subject.keywordAuthor | wavelet | - |
dc.subject.keywordAuthor | EMD | - |
dc.subject.keywordAuthor | ICA | - |
dc.subject.keywordAuthor | artificial neural network | - |
dc.subject.keywordAuthor | HOS | - |
dc.subject.keywordPlus | INDEPENDENT COMPONENT ANALYSIS | - |
dc.subject.keywordPlus | MULTIFUNCTION MYOELECTRIC CONTROL | - |
dc.subject.keywordPlus | EMPIRICAL MODE DECOMPOSITION | - |
dc.subject.keywordPlus | EMG PATTERN-RECOGNITION | - |
dc.subject.keywordPlus | WAVELET TRANSFORM | - |
dc.subject.keywordPlus | MUSCLE FATIGUE | - |
dc.subject.keywordPlus | NEURAL-NETWORKS | - |
dc.subject.keywordPlus | FEATURE-PROJECTION | - |
dc.subject.keywordPlus | NOISE-REDUCTION | - |
dc.subject.keywordPlus | MOTION ARTIFACT | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.