Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems.
Currently, the percentage of traffic accidents has increased, and according to statistics, this percentage will continue to increase every year, so it is necessary to develop new technologies to prevent this kind of accidents. This paper presents a drowsiness detection system based on electroencepha...
| Publicado en: | Revista Mexicana de Ingeniería Biomédica Vol. 45; no. 1; pp. 6 - 21 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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Sociedad Mexicana de Ingenieria Biomedica, A.C.
Jan-Apr2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=lth&AN=177354695&site=ehost-live header: @attributes: shortDbName: lth uiTerm: 177354695 longDbName: MedicLatina uiTag: AN controlInfo: bkinfo: jinfo: jid: 01889532 7L3 jtl: Revista Mexicana de Ingeniería Biomédica issn: 01889532 maglogo: N pubinfo: dt: Jan-Apr2024 vid: 45 iid: 1 pid: 20850 pub: Sociedad Mexicana de Ingenieria Biomedica, A.C. artinfo: ui: 177354695 10.17488/RMIB.45.1.1 ppf: 6 ppct: 15 formats: fmt: @attributes: type: P size: 3.4MB tig: atl: Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems. aug: au: Almirón, Rony Castillo, Bruno Adolfo Montoya Angulo, Andrés Supo, Elvis Fortunato Talavera Suarez, Jesús José Yanyachi Aco Cardenas, Daniel Domingo affil: Universidad Nacional de San Agustín de Arequipa - Perú su: Drowsiness Automobile drivers Electroencephalography Fuzzy expert systems Alpha rhythm Support vector machines Random forest algorithms K-nearest neighbor classification sug: subj: Drowsiness Automobile drivers Electroencephalography Fuzzy expert systems Alpha rhythm Support vector machines Random forest algorithms K-nearest neighbor classification keyword: drive drowsiness electroencephalogram expert systems sleepiness detection detección de somnolencia electroencefalograma sistemas expertos somnolencia en la conducción ab: Currently, the percentage of traffic accidents has increased, and according to statistics, this percentage will continue to increase every year, so it is necessary to develop new technologies to prevent this kind of accidents. This paper presents a drowsiness detection system based on electroencephalogram (EEG) signals using a pair of channels (Fp1 and Fp2) applied to drivers before entering their vehicles. First, this model detects the relationship between the area under the curve (AUC) of alpha brain waves, an effective parameter for detecting drowsiness. Then, the extracted information is passed to a fuzzy expert system (FES) that classifies the subject's state as "alert" or "sleepy"; the criterion used was a threshold and training with subjective levels. The proposed system was compared with neural network models, such as support vector machine (SVM), K nearest neighbors (KNN), and random forest (RF). Measurements of one hundred and twenty minutes were performed on each of the ten drivers for two days to test the system. The tests confirm that this system is suitable for preventive measures and that the fuzzy system is superior to traditional neural network methods. 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Revista Mexicana de Ingeniería Biomédica is the property of Sociedad Mexicana de Ingenieria Biomedica, A.C. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Revista Mexicana de Ingeniería Biomédica holder: Sociedad Mexicana de Ingenieria Biomedica, A.C. dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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