Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems

Rony Almiron; Bruno Adolfo Castillo; Andrés Montoya Angulo; Elvis Supo; Jesús José Fortunato Talavera; Daniel Domingo Yanyachi Aco Cardenas

doi:10.17488/RMIB.45.1.1

Autores/as

Rony Almiron Universidad Nacional de San Agustín de Arequipa, Perú https://orcid.org/0000-0002-3488-1761
Bruno Adolfo Castillo Universidad Nacional de San Agustín de Arequipa, Perú https://orcid.org/0000-0003-1135-2688
Andrés Montoya Angulo Universidad Nacional de San Agustín de Arequipa, Perú
Elvis Supo Universidad Nacional de San Agustín de Arequipa, Perú https://orcid.org/0000-0003-4749-8400
Jesús José Fortunato Talavera Universidad Nacional de San Agustín de Arequipa, Perú
Daniel Domingo Yanyachi Aco Cardenas Universidad Nacional de San Agustín de Arequipa, Perú

DOI:

https://doi.org/10.17488/RMIB.45.1.1

Palabras clave:

electroencefalograma, detección de somnolencia, sistemas expertos, somnolencia en la conducción

Resumen

Actualmente, el porcentaje de accidentes de tráfico ha aumentado, y según las estadísticas, este porcentaje seguirá aumentando cada año, por lo que es necesario desarrollar nuevas tecnologías para prevenir este tipo de accidentes. Este trabajo presenta un sistema de detección de somnolencia basado en señales de electroencefalograma (EEG) utilizando un par de canales (Fp1 y Fp2) aplicado a los conductores antes de entrar en sus vehículos. En primer lugar, este modelo detecta la relación entre el área bajo la curva (AUC) de las ondas cerebrales alfa, un parámetro eficaz para detectar la somnolencia. A continuación, la información extraída se pasa a un sistema experto difuso (FES) que clasifica el estado del sujeto como "alerta" o "somnoliento"; el criterio utilizado fue un umbral y el entrenamiento con niveles subjetivos. El sistema propuesto se comparó con modelos de redes neuronales, como la máquina de vectores de soporte (SVM), K vecinos más cercanos (KNN) y el bosque aleatorio (RF). Se realizaron mediciones de ciento veinte minutos en cada uno de los diez conductores durante dos días para probar el sistema. Las pruebas confirman que este sistema es adecuado para las medidas preventivas y que el sistema difuso es superior a los métodos tradicionales de redes neuronales.

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