Fuzzy fusion of eyelid activity indicators for hypovigilance-related accident prediction

被引：33

作者：

Damousis, Loannis G. ^{[1
]}

Tzovaras, Dimitrios ^{[1
]}

机构：

[1] Ctr Res & Technol Hellas, Informat & Telemat Inst, Thessaloniki 57001, Greece

来源：

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS | 2008年 / 9卷 / 03期

关键词：

driver hypovigilance; fuzzy logic; genetic algorithms (GAs); sleep prediction;

D O I：

10.1109/TITS.2008.928241

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

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.

引用

页码：491 / 500

页数：10

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