Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detection

The purpose of the present article is to characterize sEMG signals to determine muscular fatigue levels. To do this, the signal is decomposed using the discrete wavelet transform, which offers noise filtering features, simplicity and efficiency. sEMG signals on the forearm were acquired and analyzed...

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Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/8944
Acceso en línea:
https://hdl.handle.net/20.500.12585/8944
Palabra clave:
Muscle fatigue
Semg
Wavelet transform
Bioinformatics
Discrete wavelet transforms
Muscle
Signal analysis
Signal processing
Signal reconstruction
Muscle fatigues
Muscular contraction
Muscular fatigues
Noise filtering
Semg
Semg signals
Wavelet transforms
Rights
restrictedAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
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repository_id_str
spelling Lepore N.Brieva J.Garcia J.D.Romero E.Flórez-Prias L.A.Contreras Ortiz, Sonia Helena2020-03-26T16:32:38Z2020-03-26T16:32:38Z2017Proceedings of SPIE - The International Society for Optical Engineering; Vol. 1057297815106163320277786Xhttps://hdl.handle.net/20.500.12585/894410.1117/12.2285950Universidad Tecnológica de BolívarRepositorio UTB5719985778457210822856The purpose of the present article is to characterize sEMG signals to determine muscular fatigue levels. To do this, the signal is decomposed using the discrete wavelet transform, which offers noise filtering features, simplicity and efficiency. sEMG signals on the forearm were acquired and analyzed during the execution of cyclic muscular contractions in the presence and absence of fatigue. When the muscle fatigues, the sEMG signal shows a more erratic behavior of the signal as more energy is required to maintain the effort levels. © 2017 SPIE.Medical Image Computing and Computer Assisted Intervention (MICCAI);SIPAIM Foundation;Universidad Nacional de Colombia;Universidad Nacional de Colombia, Direccion de Relaciones ExterioresRecurso electrónicoapplication/pdfengSPIEhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85038430967&doi=10.1117%2f12.2285950&partnerID=40&md5=ce5142fe15a705014ee3f0d3a8bdcbb3Scopus2-s2.0-8503843096713th International Conference on Medical Information Processing and Analysis, SIPAIM 2017Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detectioninfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fMuscle fatigueSemgWavelet transformBioinformaticsDiscrete wavelet transformsMuscleSignal analysisSignal processingSignal reconstructionMuscle fatiguesMuscular contractionMuscular fatiguesNoise filteringSemgSemg signalsWavelet transforms5 October 2017 through 7 October 2017Martínez, J.A., Fatiga tipos y causas (2013) Rev. Cub. Med. Dep. & Cul. Fisica, 8 (3), pp. 1-14Phinyomark, A., Feature reduction and selection for EMG signal classification (2012) Elsevier, 39 (8), pp. 7420-7431Correa, J.L., Morales, E., Huerta, J.A., Gonzalez, J.J., Cardenas, C.R., Sistema de adquisición de señales semg para la detección de fatiga muscular (2016) Rev. Mex. de Ing. Biomedica, 37 (1), pp. 17-27Yochum, M., Bakir, T., Lepers, R., Binczak, S., Estimation of muscular fatigue under electromyostimulation using cwt (2012) IEEE Trans. on Bio. Engineering, 59 (12), pp. 3372-3378Hussain, M.S., Mamun, M., Effectiveness of the wavelet transform on the surface Emg to understand the muscle fatigue during walk (2012) Measure. Sci. Review, 12 (1), pp. 28-33Chowdhury, R.H., Reaz, M.B.I., Bin Mohd, M.A., Chellappan, K., Chang, T.G., Surface electromyography signal processing and classification techniques (2013) Mdpi Jour. Sensors, 13 (17), pp. 12431-12466Montoya, M., Surface EMG based muscle fatigue detection using a low-cost wearable sensor and amplitude frequency analysis (2015) Conf. Int. de Ingeniería, 1 (1), pp. 29-33Al-Qazzaz, N., Selection of mother wavelet functions for multi-channel EEG signal analysis during a working memory task (2015) Mdpi Jour. Sensors, 15 (11), pp. 29015-29035http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8944/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8944oai:repositorio.utb.edu.co:20.500.12585/89442023-05-25 15:52:47.478Repositorio Institucional UTBrepositorioutb@utb.edu.co
dc.title.none.fl_str_mv Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detection
title Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detection
spellingShingle Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detection
Muscle fatigue
Semg
Wavelet transform
Bioinformatics
Discrete wavelet transforms
Muscle
Signal analysis
Signal processing
Signal reconstruction
Muscle fatigues
Muscular contraction
Muscular fatigues
Noise filtering
Semg
Semg signals
Wavelet transforms
title_short Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detection
title_full Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detection
title_fullStr Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detection
title_full_unstemmed Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detection
title_sort Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detection
dc.contributor.editor.none.fl_str_mv Lepore N.
Brieva J.
Garcia J.D.
Romero E.
dc.subject.keywords.none.fl_str_mv Muscle fatigue
Semg
Wavelet transform
Bioinformatics
Discrete wavelet transforms
Muscle
Signal analysis
Signal processing
Signal reconstruction
Muscle fatigues
Muscular contraction
Muscular fatigues
Noise filtering
Semg
Semg signals
Wavelet transforms
topic Muscle fatigue
Semg
Wavelet transform
Bioinformatics
Discrete wavelet transforms
Muscle
Signal analysis
Signal processing
Signal reconstruction
Muscle fatigues
Muscular contraction
Muscular fatigues
Noise filtering
Semg
Semg signals
Wavelet transforms
description The purpose of the present article is to characterize sEMG signals to determine muscular fatigue levels. To do this, the signal is decomposed using the discrete wavelet transform, which offers noise filtering features, simplicity and efficiency. sEMG signals on the forearm were acquired and analyzed during the execution of cyclic muscular contractions in the presence and absence of fatigue. When the muscle fatigues, the sEMG signal shows a more erratic behavior of the signal as more energy is required to maintain the effort levels. © 2017 SPIE.
publishDate 2017
dc.date.issued.none.fl_str_mv 2017
dc.date.accessioned.none.fl_str_mv 2020-03-26T16:32:38Z
dc.date.available.none.fl_str_mv 2020-03-26T16:32:38Z
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
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dc.type.hasversion.none.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.spa.none.fl_str_mv Conferencia
status_str publishedVersion
dc.identifier.citation.none.fl_str_mv Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10572
dc.identifier.isbn.none.fl_str_mv 9781510616332
dc.identifier.issn.none.fl_str_mv 0277786X
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/8944
dc.identifier.doi.none.fl_str_mv 10.1117/12.2285950
dc.identifier.instname.none.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.none.fl_str_mv Repositorio UTB
dc.identifier.orcid.none.fl_str_mv 57199857784
57210822856
identifier_str_mv Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10572
9781510616332
0277786X
10.1117/12.2285950
Universidad Tecnológica de Bolívar
Repositorio UTB
57199857784
57210822856
url https://hdl.handle.net/20.500.12585/8944
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.conferencedate.none.fl_str_mv 5 October 2017 through 7 October 2017
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.rights.uri.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
dc.rights.cc.none.fl_str_mv Atribución-NoComercial 4.0 Internacional
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Atribución-NoComercial 4.0 Internacional
http://purl.org/coar/access_right/c_16ec
eu_rights_str_mv restrictedAccess
dc.format.medium.none.fl_str_mv Recurso electrónico
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv SPIE
publisher.none.fl_str_mv SPIE
dc.source.none.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038430967&doi=10.1117%2f12.2285950&partnerID=40&md5=ce5142fe15a705014ee3f0d3a8bdcbb3
Scopus2-s2.0-85038430967
institution Universidad Tecnológica de Bolívar
dc.source.event.none.fl_str_mv 13th International Conference on Medical Information Processing and Analysis, SIPAIM 2017
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