Lepore N.Brieva J.Garcia J.D.Romero E.2020-03-262020-03-262017Proceedings of SPIE - The International Society for Optical Engineering; Vol. 1057297815106163320277786Xhttps://hdl.handle.net/20.500.12585/8944The 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.Recurso electrónicoapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/conferenceObject10.1117/12.2285950Muscle fatigueSemgWavelet transformBioinformaticsDiscrete wavelet transformsMuscleSignal analysisSignal processingSignal reconstructionMuscle fatiguesMuscular contractionMuscular fatiguesNoise filteringSemgSemg signalsWavelet transformsinfo:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 InternacionalUniversidad Tecnológica de BolívarRepositorio UTB5719985778457210822856