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

dc.creatorCabezas, Holman S.
dc.creatorSarmiento, Wilson J.
dc.date2019-12-05
dc.date.accessioned2021-10-19T20:46:10Z
dc.date.available2021-10-19T20:46:10Z
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1513
dc.identifier10.22430/22565337.1513
dc.identifier.urihttp://hdl.handle.net/20.500.12622/5461
dc.descriptionThis paper compares the results of three computational models (pattern recognition, hidden Markov models, and bag of features) for recognizing the hand gestures of a user with reduced mobility using biometric signal processing. The evaluation of the models included eight gestures co-designed with a person with reduced mobility. The models were evaluated using a cross-validation scheme, calculating sensitivity and precision metrics, and a data set of ten repetitions of each gesture. It can be concluded that the bag-of-features model achieved the best performance considering the two metrics under evaluation; the traditional pattern recognition model, using vector support machines, produced the most stable results; and the hidden Markov models had the lowest performance.en-US
dc.descriptionEste 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.es-ES
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dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1513/1471
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1513/1561
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1513/1575
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dc.sourceTecnoLógicas; Vol. 22 (2019): Special issue-2019; 33-47en-US
dc.sourceTecnoLógicas; Vol. 22 (2019): Edición especial-2019; 33-47es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectGesture recognitionen-US
dc.subjectHuman computer interactionen-US
dc.subjectSignal processingen-US
dc.subjectMachine learningen-US
dc.subjectPattern recognitionen-US
dc.subjectReconocimiento de gestoses-ES
dc.subjectinteracción hombre-máquinaes-ES
dc.subjectprocesamiento de señaleses-ES
dc.subjectaprendizaje computacionales-ES
dc.subjectreconocimiento de patroneses-ES
dc.titleEvaluation of Models for Gesture Recognition from Biometric Signals of a Person with Reduced Mobilityen-US
dc.titleEvaluación de modelos para el reconocimiento de gestos en señales biométricas, para un usuario con movilidad reducidaes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


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