Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures

Abstract

Abstract-This paper presents a real-time method based on various entropy and complexity measures for detection and identification of driving fatigue from recorded electroencephalogram (EEG), electromyogram, and electrooculogram signals. The complexity features were used to distinguish whether the subjects are experienced drivers by calculating the Lempel-Ziv complexity of EEG approximate entropy (ApEn). Different threshold values can be set for the two kinds of drivers individually. The entropy-based features, namely, the wavelet entropy (WE), the peak-to-peak value of ApEn (PP-ApEn), and the peak-to-peak value of sample entropy (PP-SampEn), were extracted from the collected signals to estimate the driving fatigue stages. We proposed WE in a sliding window (WES), PP-ApEn in a sliding window (PP-ApEnS), and PP-SampEn in a sliding window (PP-SampEnS) for real-time analysis of driver fatigue. The real-time features obtained by WE, PP-ApEn, and PP-SampEn with sliding window were applied to artificial neural network for training and testing the system, which gives four situations for the fatigue level of the subjects, namely, normal state, mild fatigue, mood swing, and excessive fatigue. Then, the driver fatigue level can be determined in real time. The accuracy of estimation is about 96.5%-99.5%. Receiver operating characteristic (ROC) curve was used to present the performance of the neural network classifier. The area under the ROC curve is 0.9931. The results show that the developed method is valuable for the application of avoiding some traffic accidents caused by driver's fatigue.Index Terms-Driver fatigue, electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG), entropy, neural network.

A New Method to Detect Driver Fatigue Based on EMG and ECG Collected by Portable Non-Contact Sensors

“…By repeated testing, the SampEn of ECG has the best discrimination on driver fatigue when the parameters used in the present work are chosen as m=2 and r=0.15×SD (SD is the standard deviation of the original data) [17,18].…”

Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

“…Intrusive detection analyzes the psychological state of the driver through electroencephalographic (EEG) and electrooculographic (EOG) information features [3,4,5,6,7,8]. Generally, the fatigue detection systems based on EEG and EOG signals provide high accuracy; however, they rely on physiological information measured by sensors located on or around the driver, and the driver’s movement therefore affects the reliability of data collection.…”

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

“…The indexes and the related classification performance adopted in previous studies are listed in Table 4. Zhang [37] used the approximate entropy and combined a variety of physiological signals for 20 subjects, by using the method of multi feature combined analysis method with a high success rate of 96.5%. Khushaba [38] employed 31 subjects to participate in the study, combining EEG and EOG to analyze with the final classification accuracy of 95%.…”

“…Zhao [39] used the sample entropy to study EEG signals. The results of reference [37][38][39] show that using various physiological features and a variety of entropy fusion method could obtain an obvious improvement for the classification accuracy. However, various physiological features could increase the difficulty of signal acquisition and result in a difficult comparison with different entropies for different signal sources.…”