IEEE Transactions on Intelligent Transportation Systems
Applied Computational Intelligence and Soft Computing
Four machine learning algorithms for biometrics fusion: a comparative study
Applied Computational Intelligence and Soft Computing
Detecting driver drowsiness using feature-level fusion and user-specific classification
Expert Systems with Applications: An International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, a fuzzy expert system (FES) for the detection of the physiological manifestations of extreme hypovigilance is presented. A large number of features that describe the eyelid activity of drivers is examined, and fuzzy logic is used for the fusion of the most prominent features to not only increase the accident prediction accuracy but also provide a reliable system that generates a small number of false warnings. For the development and testing of the system, driving simulator data from 35 drowsy subjects were used. In addition, a secondary control group of 13 alert drivers was used for the estimation of the trained system's false alarm ratio. The results show that a fuzzy combination of eyelid activity parameters may lead to a system with high sensitivity and specificity in predicting sleep onset and related accidents.