Clasificación de gestos de la lengua de señas colombiana a partir del análisis de señales electromiográficas utilizando Redes neuronales artificiales
| dc.contributor.author | Galvis Serrano, Elvis H. | |
| dc.contributor.author | Sánchez Galvis, Iván | |
| dc.contributor.author | Flórez, Natalia | |
| dc.contributor.author | Zabala Vargas, Sergio | |
| dc.contributor.cvlac | https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001390461 | |
| dc.contributor.cvlac | https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000754129 | |
| dc.contributor.cvlac | https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001359761 | |
| dc.contributor.googlescholar | https://scholar.google.com/citations?user=mn8hUGIAAAAJ&hl=es&oi=sra | |
| dc.contributor.googlescholar | https://scholar.google.com/citations?user=zXdEI0gAAAAJ&hl=es&oi=ao | |
| dc.contributor.googlescholar | https://scholar.google.com/citations?hl=es&user=HHVpOlsAAAAJ | |
| dc.contributor.orcid | 0000-0003-2518-4769 | |
| dc.date.accessioned | 2020-05-19T18:05:30Z | |
| dc.date.available | 2020-05-19T18:05:30Z | |
| dc.date.issued | 2020-05-18 | |
| dc.description | El objetivo del presente trabajo es clasificar los 27 gestos del alfabeto de señas colombiano, mediante un clasificador de redes neuronales artificiales a partir de señales electromiográficas. El clasificador fue diseñado en cuatro fases: 1) Adquisición de señales electromiográficas provenientes de los ocho sensores de la manilla Myo Armband, 2) Extracción de características de las señales electromiográficas empleando la transformada Wavelet de Paquetes, 3) Entrenamiento de la red neuronal y 4) Validación del método de clasificación utilizando la técnica de validación cruzada. Para el presente estudio se adquirieron registros de señales electromiográficas de 13 sujetos con discapacidad auditiva. El clasificador presentó un porcentaje de precisión promedio de 88,4%, muy similar a otros métodos de clasificación presentados en la literatura. El método de clasificación puede ser escalado para clasificar, adicional a los 27 gestos, el vocabulario de la lengua de señas colombiana. | spa |
| dc.description.abstract | The objective of this article is to classify the 27 gestures of the Colombian sign alphabet, by means of a classifier of artificial neural networks based on electromyographic signals. The classifier was designed in four phases: Acquisition of electromyographic signals from the eight sensors of the Myo Armband handle, extraction of characteristics of the electromyographic signals using the wavelet transform of packages, training of the neural network and validation of the classification method using the cross-validation technique. For the present study, records of electromyographic signals from 13 subjects with hearing impairment were acquired. The classifier presented an average accuracy percentage of 88.4%, very similar to other classification methods presented in the literature. The classification method can be scaled to classify, in addition to the 27 gestures, the vocabulary of the Colombian sign language. | spa |
| dc.description.domain | http://unidadinvestigacion.usta.edu.co | spa |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Galvis-Serrano, E. H., Sánchez-Galvis, I., Flórez, N., & Zabala-Vargas, S. (2019). Clasificación de gestos de la lengua de señas colombiana a partir del análisis de señales electromiográficas utilizando redes neuronales artificiales. Información Tecnológica, 30(2), 171-180 | spa |
| dc.identifier.doi | https://doi.org/10.4067/S0718-07642019000200171 | spa |
| dc.identifier.uri | http://hdl.handle.net/11634/23310 | |
| dc.publisher.branch | CRAI-USTA Bogotá | spa |
| dc.relation.annexed | https://repository.usta.edu.co/handle/11634/13021 | spa |
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| dc.rights | Atribución-NoComercial-SinDerivadas 2.5 Colombia | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/2.5/co/ | |
| dc.subject.keyword | Colombian sign language; | spa |
| dc.subject.keyword | Myo Armband | spa |
| dc.subject.keyword | neural networks | spa |
| dc.subject.keyword | cross validation | spa |
| dc.subject.keyword | Wavelet | spa |
| dc.subject.proposal | lengua de señas colombiana | spa |
| dc.subject.proposal | redes neuronales; | spa |
| dc.subject.proposal | Myo Armband; | spa |
| dc.subject.proposal | validación cruzada | spa |
| dc.subject.proposal | Wavelet | spa |
| dc.title | Clasificación de gestos de la lengua de señas colombiana a partir del análisis de señales electromiográficas utilizando Redes neuronales artificiales | spa |
| dc.type.category | Apropiación Social y Circulación del Conocimiento: Edición de revista o libro de divulgación científica | spa |