Abstract
This work presents a system for detecting excess alcohol in drivers to reduce road traffic accidents. To do so, criteria such as alcohol concentration the environment, a facial temperature of the driver and width of the pupil are considered. To measure the corresponding variables, the data acquisition procedure uses sensors and artificial vision. Subsequently, data analysis is performed into stages for prototype selection and supervised classification algorithms. Accordingly, the acquired data can be stored and processed in a system with low-computational resources. As a remarkable result, the amount of training samples is significantly reduced, while an admissible classification performance is achieved - reaching then suitable settings regarding the given device’s conditions.
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This work is supported by the “Smart Data Analysis Systems - SDAS” group (http://sdas-group.com).
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Rosero-Montalvo, P.D., López-Batista, V.F., Peluffo-Ordóñez, D.H., Erazo-Chamorro, V.C., Arciniega-Rocha, R.P. (2019). Multivariate Approach to Alcohol Detection in Drivers by Sensors and Artificial Vision. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science(), vol 11487. Springer, Cham. https://doi.org/10.1007/978-3-030-19651-6_23
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DOI: https://doi.org/10.1007/978-3-030-19651-6_23
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