Abstract
The aim of this work is to generate a Gold Standard signal to assess the alertness state of drivers based on the Electrooculogram (EOG) dynamics derived from a polysomnography device. Different EOG patterns have been analyzed in order to determine the relation between ocular activity and sleep onset while doing complex tasks. More than 15 h of laboratory tests were analyzed in order to detect drowsiness while doing different cognitive activities. The proposed method has a sensitivity of 92.41 % and a Predictive Positive Value (VPP) of 93.41 % in detecting drowsiness in laboratory conditions. The results show that the proposed index may be promising to assess the alertness state of real drivers.
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Acknowledgements
This work has been partially funded by the Spanish MINISTERIO DE CIENCIA E INNOVACIÓN. Project IPT-2011-0833-900000. Healthy Life style and Drowsiness Prevention-HEALING DROP.
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Rodríguez-Ibáñez, N., Meca-Calderón, P., García-González, M.A., Ramos-Castro, J., Fernández-Chimeno, M. (2014). Gold Standard Generation Using Electrooculogram Signal for Drowsiness Detection in Simulator Conditions. In: Fernández-Chimeno, M., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2013. Communications in Computer and Information Science, vol 452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44485-6_6
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