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Article

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

1
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro, Zacatecas 98000, Mexico
2
Cátedras-CONACyT, Consejo Nacional de Ciencia y Tecnología, Ciudad de México 03940, Mexico
3
Escuela de Ingeniería y Tecnologías, Universidad de Monterrey, San Pedro Garza García 66238, Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Alexey Kashevnik, Andrei Gurtov, Sara Ferreira and Felipe Jiménez
Sensors 2021, 21(22), 7752; https://doi.org/10.3390/s21227752
Received: 27 September 2021 / Revised: 9 November 2021 / Accepted: 16 November 2021 / Published: 21 November 2021
(This article belongs to the Special Issue Smartphone Sensors for Driver Behavior Monitoring Systems)
Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions. View Full-Text
Keywords: drinking and driving; smart vehicle; smart infotainment; alcohol detection; genetic algorithm drinking and driving; smart vehicle; smart infotainment; alcohol detection; genetic algorithm
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MDPI and ACS Style

Celaya-Padilla, J.M.; Romero-González, J.S.; Galvan-Tejada, C.E.; Galvan-Tejada, J.I.; Luna-García, H.; Arceo-Olague, J.G.; Gamboa-Rosales, N.K.; Sifuentes-Gallardo, C.; Martinez-Torteya, A.; De la Rosa, J.I.; Gamboa-Rosales, H. In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection. Sensors 2021, 21, 7752. https://doi.org/10.3390/s21227752

AMA Style

Celaya-Padilla JM, Romero-González JS, Galvan-Tejada CE, Galvan-Tejada JI, Luna-García H, Arceo-Olague JG, Gamboa-Rosales NK, Sifuentes-Gallardo C, Martinez-Torteya A, De la Rosa JI, Gamboa-Rosales H. In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection. Sensors. 2021; 21(22):7752. https://doi.org/10.3390/s21227752

Chicago/Turabian Style

Celaya-Padilla, Jose M., Jonathan S. Romero-González, Carlos E. Galvan-Tejada, Jorge I. Galvan-Tejada, Huizilopoztli Luna-García, Jose G. Arceo-Olague, Nadia K. Gamboa-Rosales, Claudia Sifuentes-Gallardo, Antonio Martinez-Torteya, José I. De la Rosa, and Hamurabi Gamboa-Rosales. 2021. "In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection" Sensors 21, no. 22: 7752. https://doi.org/10.3390/s21227752

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