Desenvolvimento de uma armband para captura de sinais eletromiográficos para reconhecimento de movimentos

• Ano de defesa: 2016
• Autor(a) principal: Mendes Júnior, José Jair Alves
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Ponta Grossa; Brasil; Programa de Pós-Graduação em Engenharia Elétrica; UTFPR
• Palavras-chave em Português: Eletromiografia; Redes neurais (Neurobiologia); Sistemas de reconhecimento de padrões; Engenharia elétrica; Electromyography; Neural networks (Neurobiology); Pattern recognition systems; Electric engineering; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA; Engenharia Elétrica
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/2435

This master thesis presents the development of an armband system to capture of superficial electromyography signals to arm movement recognition. All project steps, since the physical building, project of the circuits, acquisition system, processing and classification by Artificial Neural Networks are presented. An armband with eight channel to capture the electromyography signal was constructed and an auxiliary system (gyroscope) was used to indicate the instant when the arm was moved. The muscle acquired groups were the biceps and triceps. By sensor data fusion, the signals were processed by LabVIEWTM routines. After the signal characteristic extraction, the samples were forwarded to a Multi-Layer Perceptron Neural Network to movement classification of arm flexion and extension. The same armband was inserted on the forearm and the electromyography signals were compared with the signals obtained by the commercial device MyoTM. The system presented, as results, high classification rates, above of 95% and the obtained signals on the region of forearm showed similarities with the obtained ones by commercial device.