Diseño e implementación de un sistema de adquisición, procesamiento y clasificación de señal mioeléctrica para prótesis transhumeral de tres grados de libertad
This work presents the design and implementation of the necessary blocks for the acquisition, processing and classification of myoelectric signals, with application to control of transhumeral prosthesis of three degrees-of-freedom. In first place, the objectives and motivation of this work are estab...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2008 |
| País: | México |
| Institución: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | español |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/407 |
| Acceso en línea: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/407 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/Sistemas de control médico/Medical control systems info:eu-repo/classification/Interferencia (onda)/Interference(wave) info:eu-repo/classification/análisis estadístico/Statistical analysis info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/22 info:eu-repo/classification/cti/2203 |
| Sumario: | This work presents the design and implementation of the necessary blocks for the acquisition, processing and classification of myoelectric signals, with application to control of transhumeral prosthesis of three degrees-of-freedom. In first place, the objectives and motivation of this work are established. Then the necessary theoretic fundamentals referent to myoelectric signal and its use in prosthetic control are presented. Afterwards, there are shown the design of the myoelectric amplifier and the realization of a graphic interface in MATLAB, which can acquire the previously amplified and filtered data through the computer sound card. Later the time domain characteristics utilized in the classification are shown. With respect to the classification, two options are presented: a level classifier and a classifier realized by means of a multilayer perceptron neural network. Subsequently the obtained results and conclusions are presented. |
|---|