Abstract
Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1–2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver.
Highlights
Detection of driver fatigue using electronic and information technology is a meaningful research topic for driving safety assistance systems [1, 2]
To evaluate the performance influence on different combined entropies, we calculated the results of different entropy fusion between PE, Approximate entropy (AE), Sample entropy (SE) and Fuzzy entropy (FE) as features via the support vector machine (SVM) classifier
An objective approach based on multiple entropy fusion analysis was proposed to detect driver fatigue in an EEG-based system
Summary
Detection of driver fatigue using electronic and information technology is a meaningful research topic for driving safety assistance systems [1, 2]. Driver fatigue is one of the most important factors in traffic accidents. After driving for an extensive period, people experience fatigue, which decreases their reaction times during emergencies and contributes to casualty accidents. Some studies reveal that 15%-20% of all fatal traffic accidents are related to driver fatigue, and recent statistics estimate that 1,200 deaths and 76,000 injuries can be attributed to fatigue-related crashes annually [3]. Promoting technologies for the detection or prevention of driver fatigue is crucial. The emergence of artificial intelligence and the rapid development of electronic and information technology provide opportunities for detecting driver fatigue. Recognition rates that are based on subjective detection methods are greatly influenced by a driver’s own judgment or the actions of other drivers, and real-time detection
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