Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems.
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| Title: | Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems. |
|---|---|
| Alternate Title: | Detección Preventiva de la Somnolencia del Conductor a partir de Señales EEG Mediante Sistemas Expertos Difusos. |
| Authors: | Almirón, Rony1 ralmirona@unsa.edu.pe, Castillo, Bruno Adolfo1, Montoya Angulo, Andrés1, Supo, Elvis1, Fortunato Talavera Suarez, Jesús José1, Yanyachi Aco Cardenas, Daniel Domingo1 |
| Source: | Revista Mexicana de Ingeniería Biomédica. Jan-Apr2024, Vol. 45 Issue 1, p6-20. 15p. |
| Subjects: | DROWSINESS, AUTOMOBILE drivers, ELECTROENCEPHALOGRAPHY, FUZZY expert systems, ALPHA rhythm, SUPPORT vector machines, RANDOM forest algorithms, K-nearest neighbor classification |
| Abstract (English): | 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. [ABSTRACT FROM AUTHOR] |
| Abstract (Spanish): | 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. [ABSTRACT FROM AUTHOR] |
| 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. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | MedicLatina |
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| Header | DbId: lth DbLabel: MedicLatina An: 177354695 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: Detección Preventiva de la Somnolencia del Conductor a partir de Señales EEG Mediante Sistemas Expertos Difusos. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Almirón%2C+Rony%22">Almirón, Rony</searchLink><relatesTo>1</relatesTo><i> ralmirona@unsa.edu.pe</i><br /><searchLink fieldCode="AR" term="%22Castillo%2C+Bruno+Adolfo%22">Castillo, Bruno Adolfo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Montoya+Angulo%2C+Andrés%22">Montoya Angulo, Andrés</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Supo%2C+Elvis%22">Supo, Elvis</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Fortunato+Talavera+Suarez%2C+Jesús+José%22">Fortunato Talavera Suarez, Jesús José</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yanyachi+Aco+Cardenas%2C+Daniel+Domingo%22">Yanyachi Aco Cardenas, Daniel Domingo</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Revista+Mexicana+de+Ingeniería+Biomédica%22">Revista Mexicana de Ingeniería Biomédica</searchLink>. Jan-Apr2024, Vol. 45 Issue 1, p6-20. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22DROWSINESS%22">DROWSINESS</searchLink><br /><searchLink fieldCode="DE" term="%22AUTOMOBILE+drivers%22">AUTOMOBILE drivers</searchLink><br /><searchLink fieldCode="DE" term="%22ELECTROENCEPHALOGRAPHY%22">ELECTROENCEPHALOGRAPHY</searchLink><br /><searchLink fieldCode="DE" term="%22FUZZY+expert+systems%22">FUZZY expert systems</searchLink><br /><searchLink fieldCode="DE" term="%22ALPHA+rhythm%22">ALPHA rhythm</searchLink><br /><searchLink fieldCode="DE" term="%22SUPPORT+vector+machines%22">SUPPORT vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22RANDOM+forest+algorithms%22">RANDOM forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Spanish) Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>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. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17488/RMIB.45.1.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 6 Subjects: – SubjectFull: DROWSINESS Type: general – SubjectFull: AUTOMOBILE drivers Type: general – SubjectFull: ELECTROENCEPHALOGRAPHY Type: general – SubjectFull: FUZZY expert systems Type: general – SubjectFull: ALPHA rhythm Type: general – SubjectFull: SUPPORT vector machines Type: general – SubjectFull: RANDOM forest algorithms Type: general – SubjectFull: K-nearest neighbor classification Type: general Titles: – TitleFull: Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Almirón, Rony – PersonEntity: Name: NameFull: Castillo, Bruno Adolfo – PersonEntity: Name: NameFull: Montoya Angulo, Andrés – PersonEntity: Name: NameFull: Supo, Elvis – PersonEntity: Name: NameFull: Fortunato Talavera Suarez, Jesús José – PersonEntity: Name: NameFull: Yanyachi Aco Cardenas, Daniel Domingo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan-Apr2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 01889532 Numbering: – Type: volume Value: 45 – Type: issue Value: 1 Titles: – TitleFull: Revista Mexicana de Ingeniería Biomédica Type: main |
| ResultId | 1 |
