Evaluación de modelos para el reconocimiento de gestos en señales biométricas, para un usuario con movilidad reducida

Palabras clave: Reconocimiento de gestos, interacción hombre-máquina, procesamiento de señales, aprendizaje computacional, reconocimiento de patrones

Resumen

Este trabajo presenta los resultados de una comparación de tres modelos computaciones (reconocimiento de patrones, modelos ocultos de Markov y bolsas de características), para el reconocimiento de gestos por medio del procesamiento de señales biométricas, para un usuario con movilidad reducida. La evaluación involucra ocho gestos diseñados de forma participativa con un usuario con problemas de movilidad y se desarrolló mediante un esquema de validación cruzada, en el que se calcularon métricas de sensibilidad y precisión, para un conjunto de datos formado por diez repeticiones de cada gesto. Los resultados obtenidos permitieron concluir que las bolsas de características son el modelo con mejor desempeño para las dos métricas evaluadas. El modelo de tradicional de reconocimiento de patrones al usar máquinas de soporte vectorial mostró los resultados más estables y los modelos ocultos de Markov presentaron el desempeño más bajo.

Biografía del autor/a

Holman S. Cabezas, Universidad Militar Nueva Granada, Colombia

Ingeniero en Multimedia, Grupo de Investigación en Multimedia -GIM, Facultad de Ingeniería, Universidad Militar Nueva Granada, Bogotá-Colombia, u1201569@unimilitar.edu.co

Wilson J. Sarmiento*, Universidad Militar Nueva Granada, Colombia

PhD. en Ciencias de la Electrónica-Áreas Computación, Grupo de Investigación en Multimedia -GIM, Facultad de Ingeniería, Universidad Militar Nueva Granada, Bogotá- Colombia, wilson.sarmiento@unimilitar.edu.co

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Cómo citar
[1]
H. S. . Cabezas y W. J. Sarmiento, «Evaluación de modelos para el reconocimiento de gestos en señales biométricas, para un usuario con movilidad reducida», TecnoL., vol. 22, pp. 33–47, dic. 2019.

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Publicado
2019-12-05
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Artículos de investigación

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