Caracterização da fadiga a partir do processamento de sinais mioelétricos e sua utilização no diagnóstico da síndrome da fibromialgia

• Ano de defesa: 2013
• Autor(a) principal: Moreira, Marcelo Hubner
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Biotecnologia; Centro de Ciências da Saúde; UFES; Programa de Pós-Graduação em Biotecnologia
• Palavras-chave em Português: Myoelectric signal; Fatigue; Fibromyalgia; Isometric; Isotonic; Sinais mioelétricos; Fadiga; Fibromialgia; Isométrico; Isotônico; Biotecnologia; 61
• Link de acesso: http://repositorio.ufes.br/handle/10/5756

This work aims to characterize the fatigue from myoelectric signals and use them as an aid to the diagnosis of rheumatic diseases such as Fibromyalgia. The condition for this is the analysis of muscle fatigue. Through the evaluation of myoelectric signals, the behavior of muscle in some work situations was measured, such as isotonic and isometric muscle contraction, which describes the static and dynamic motor behavior. With the myoelectric signals, digital filtering techniques were applied to mitigate the noise corrupting the myoelectric signal. Then some algorithms were implemented to detect fatigue. With that, a protocol for assessing motor response based on the condition of muscle fatigue was established. In this situation, with the working muscle, the myoelectric signal acquisition was made from surface electrodes, using a commercial acquisition system. The data were processed in MATLAB R platform; algorithms were implemented for the identification of fatigue, such as RMS, MNF, ARV, MDF and AIF. In the final result, it was found that for both isometric tasks and isotonic tasks, it is recommended the use of constant weight with 60% of MCV, using MNF and RMS indicators, which were the most consistent indicators among them.