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...
| Main Authors: | , , |
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2015
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/3554 |
| _version_ | 1801706070460071936 |
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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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