Fuzzy Fusion of Eyelid Activity Indicators for Hypovigilance-Related Accident Prediction

• Published in: IEEE transactions on intelligent transportation systems Vol. 9; no. 3; pp. 491 - 500
• Main Authors: Damousis, I.G., Tzovaras, D.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.09.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accidents; Accuracy; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Control systems; Driver hypovigilance; Exact sciences and technology; Eyelids; Fusion power generation; Fuzzy logic; Fuzzy systems; genetic algorithms (GAs); Ground, air and sea transportation, marine construction; Hybrid intelligent systems; Pattern recognition. Digital image processing. Computational geometry; Road transportation and traffic; Sensitivity and specificity; sleep prediction; System testing; Driver hypovigilance; genetic algorithms (GAs); fuzzy logic; sleep prediction; Vigilance; Image processing; Vehicle driving; Expert system; Eye movement; False alarm rate; Fuzzy logic; Sleep; Accident; Genetic algorithm; High sensitivity; Alarm system; Simulator
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2008.928241

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.