Electronic control arm using electromyographic signals

The studies focused in pattern extractions of electromyography signals (SEMG) has been growing, due to their multiple applications. This paper presents an electronic system implementation for the SEMG recording of a subject upper extremity in order to remotely control an electronic arm. Initially, w...

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Main Authors:	García-Pinzón, Jorge Andrés, Mendoza, Luis Enrique, Flórez, Elkin Gregorio
Format:	Online
Language:	spa
Published:	Universidad Pedagógica y Tecnológica de Colombia 2015
Subjects:	electronic arm control electromyography ANR SVM patterns extraction wavelet transformed Brazo electrónico Electromiografía Extracción de patrones MSV RNA Transformada wavelet.
Online Access:	https://revistas.uptc.edu.co/index.php/ingenieria/article/view/3554
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author	García-Pinzón, Jorge Andrés Mendoza, Luis Enrique Flórez, Elkin Gregorio
author_facet	García-Pinzón, Jorge Andrés Mendoza, Luis Enrique Flórez, Elkin Gregorio
author_sort	García-Pinzón, Jorge Andrés
collection	OJS
description	The studies focused in pattern extractions of electromyography signals (SEMG) has been growing, due to their multiple applications. This paper presents an electronic system implementation for the SEMG recording of a subject upper extremity in order to remotely control an electronic arm. Initially, we performed a signals preprocessing, to remove the less important information and to recognize the interest areas. Then the patterns were extracted and classified. The techniques used were: The wavelet analysis (AW), the principal components analysis (PCA), the Fourier transformed (FT), the discrete cosine transformed (DCT), the support vector machines (SVM) and the artificial neural networks (ANR). In this paper we demonstrated, that the methodology stated, allows to realize a process of classification with a superior performance to 95%. There were recorded more than four thousands signals.
format	Online
id	oai:oai.revistas.uptc.edu.co:article-3554
institution	Revista Facultad de Ingeniería
language	spa
publishDate	2015
publisher	Universidad Pedagógica y Tecnológica de Colombia
record_format	ojs
spelling	oai:oai.revistas.uptc.edu.co:article-35542018-11-21T00:48:30Z Electronic control arm using electromyographic signals Control de brazo electrónico usando señales electromiográficas García-Pinzón, Jorge Andrés Mendoza, Luis Enrique Flórez, Elkin Gregorio electronic arm control electromyography ANR SVM patterns extraction wavelet transformed Brazo electrónico Electromiografía Extracción de patrones MSV RNA Transformada wavelet. The studies focused in pattern extractions of electromyography signals (SEMG) has been growing, due to their multiple applications. This paper presents an electronic system implementation for the SEMG recording of a subject upper extremity in order to remotely control an electronic arm. Initially, we performed a signals preprocessing, to remove the less important information and to recognize the interest areas. Then the patterns were extracted and classified. The techniques used were: The wavelet analysis (AW), the principal components analysis (PCA), the Fourier transformed (FT), the discrete cosine transformed (DCT), the support vector machines (SVM) and the artificial neural networks (ANR). In this paper we demonstrated, that the methodology stated, allows to realize a process of classification with a superior performance to 95%. There were recorded more than four thousands signals. Los trabajos enfocados en la extracción de patrones en señales electromiográficas (SEMG) han venido creciendo debido a sus múltiples aplicaciones. En este artículo se presenta una aplicación en la cual se implementa un sistema electrónico para el registro de las SEMG de la extremidad superior en un sujeto, con el fin de controlar de forma remota un brazo electrónico. Se realizó una etapa de preprocesamiento de las señales registradas, para eliminar información poco relevante, y reconocimiento de zonas de interés, enseguida se extraen los patrones y se clasifican. Las técnicas utilizadas fueron: análisis wavelet (AW), análisis de componentes principales (ACP), transformada de fourier (TF), transformada del coseno discreta (TDC), energía, máquinas de soporte vectorial (MSV o SVM) y redes neuronales (RNA). En este artículo se demuestra que la metodología planteada permite realizar un proceso de clasificación con un rendimiento superior al 95%. Se registraron más de 4000 señales. Universidad Pedagógica y Tecnológica de Colombia 2015-05-05 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion investigation application/pdf text/html https://revistas.uptc.edu.co/index.php/ingenieria/article/view/3554 10.19053/01211129.3554 Revista Facultad de Ingeniería; Vol. 24 No. 39 (2015); 71-84 Revista Facultad de Ingeniería; Vol. 24 Núm. 39 (2015); 71-84 2357-5328 0121-1129 spa https://revistas.uptc.edu.co/index.php/ingenieria/article/view/3554/3164 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/3554/4327
spellingShingle	electronic arm control electromyography ANR SVM patterns extraction wavelet transformed Brazo electrónico Electromiografía Extracción de patrones MSV RNA Transformada wavelet. García-Pinzón, Jorge Andrés Mendoza, Luis Enrique Flórez, Elkin Gregorio Electronic control arm using electromyographic signals
title	Electronic control arm using electromyographic signals
title_alt	Control de brazo electrónico usando señales electromiográficas
title_full	Electronic control arm using electromyographic signals
title_fullStr	Electronic control arm using electromyographic signals
title_full_unstemmed	Electronic control arm using electromyographic signals
title_short	Electronic control arm using electromyographic signals
title_sort	electronic control arm using electromyographic signals
topic	electronic arm control electromyography ANR SVM patterns extraction wavelet transformed Brazo electrónico Electromiografía Extracción de patrones MSV RNA Transformada wavelet.
topic_facet	electronic arm control electromyography ANR SVM patterns extraction wavelet transformed Brazo electrónico Electromiografía Extracción de patrones MSV RNA Transformada wavelet.
url	https://revistas.uptc.edu.co/index.php/ingenieria/article/view/3554
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