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

dc.contributorLepore N.
dc.contributorBrieva J.
dc.contributorGarcia J.D.
dc.contributorRomero E.
dc.creatorFlórez-Prias L.A.
dc.creatorContreras Ortiz, Sonia Helena
dc.date.accessioned2020-03-26T16:32:38Z
dc.date.available2020-03-26T16:32:38Z
dc.date.created2020-03-26T16:32:38Z
dc.date.issued2017
dc.identifierProceedings of SPIE - The International Society for Optical Engineering; Vol. 10572
dc.identifier9781510616332
dc.identifier0277786X
dc.identifierhttps://hdl.handle.net/20.500.12585/8944
dc.identifier10.1117/12.2285950
dc.identifierUniversidad Tecnológica de Bolívar
dc.identifierRepositorio UTB
dc.identifier57199857784
dc.identifier57210822856
dc.description.abstractThe 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.
dc.languageeng
dc.publisherSPIE
dc.relation5 October 2017 through 7 October 2017
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rightsAtribución-NoComercial 4.0 Internacional
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85038430967&doi=10.1117%2f12.2285950&partnerID=40&md5=ce5142fe15a705014ee3f0d3a8bdcbb3
dc.sourceScopus2-s2.0-85038430967
dc.source13th International Conference on Medical Information Processing and Analysis, SIPAIM 2017
dc.titleAnalysis of sEMG signals using discrete wavelet transform for muscle fatigue detection


Este ítem pertenece a la siguiente institución