Detalhes bibliográficos
| Ano de defesa: |
2016 |
| Autor(a) principal: |
Moraes, Rodrigo Belagamba de
 |
| Orientador(a): |
Salton, Aurélio Tergolina
|
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Pontifícia Universidade Católica do Rio Grande do Sul
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Engenharia Elétrica
|
| Departamento: |
Faculdade de Engenharia
|
| País: |
Brasil
|
| Palavras-chave em Português: |
|
| Área do conhecimento CNPq: |
|
| Link de acesso: |
http://tede2.pucrs.br/tede2/handle/tede/6901
|
Resumo: |
This study proposes a new methodology for automatic detection of muscle activation in electromyographic (EMG) signals. Which uses the local variance of EMG signal to determine the onset and offset times of muscle activation events. Were implemented two existing and consolidated methods (Teager-Kaiser Energy Operator and Sample Entropy) in order to carry the activation detection by another way and enable a comparative analysis of different methodologies. The evaluation of results was separated into two stages: performance analysis and convergence analysis. The performance analysis was established by quantifiable and objective parameters: accuracy, tolerance to noise and computational cost. It was developed also a generator of synthetic EMG signals whose muscle activation times and signal to noise ratio (SNR) were previously known. Considering the parameters established and the data analyzed, the proposed methodology demonstrated a better precision and tolerance to noise when compared to the others methods. The convergence analysis used the real EMG data from ten subjects, of which was collected signals from eight different muscles. Through this set of data, it was possible to demonstrate the high correlation between the results from the analyzed methods. |
