Multivariate approach to alcohol detection in drivers by sensors and artificial vision
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Autores: Paul David Rosero Montalvo, Vivian Félix López Batista
, Diego Hernán Peluffo Ordoñez , Vanessa Erazo Chamorro, Ricardo Arciniega Rocha
- Localización: From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II / coord. por Hojjat Adeli; José Manuel Ferrández Vicente (dir. congr.) , José Ramón Álvarez Sánchez (dir. congr.) , Félix de la Paz López (dir. congr.) , Francisco Javier Toledo Moreo (dir. congr.), 2019, ISBN 978-3-030-19651-6, págs. 234-243
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
This work presents a system for detecting excess alcohol indrivers 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.
