Lin Wang
1) Northeastern University
2) Shenyang Institute of Engineering
Hong Wang
Northeastern University
Xin Jiang
Northeastern University
Recently, detection and prediction on driver fatigue have become interest of research worldwide. In the present work, a new method is built to effectively evaluate driver fatigue based on electromyography (EMG) and electrocardiogram (ECG) collected by portable real-time and non-contact sensors. First, under the non-disturbance condition for driver’s attention, mixed physiological signals (EMG, ECG and artefacts) are collected by non-contact sensors located in a cushion on the driver’s seat. EMG and ECG are effectively separated by FastICA, and de-noised by empirical mode decomposition (EMD). Then, three physiological features, complexity of EMG, complexity of ECG, and sample entropy (SampEn) of ECG, are extracted and analysed. Principal components are obtained by principal components analysis (PCA) and are used as independent variables. Finally, a mathematical model of driver fatigue is built, and the accuracy of the model is up to 91%. Moreover, based on the questionnaire, the calculation results of model are consistent with real fatigue felt by the participants. Therefore, this model can effectively detect driver fatigue.
Santamaria J, Chiappa KH. The EEG of Drowsiness. New York: Demos Publications; 1987.
Lemke M. Correlation between EEG and driver’s actions during prolonged driving under monotonous conditions. Accident Analysis & Prevention. 1982;14(1):7-17.
Fu RR, Wang H, Zhao WB. Dynamic driver fatigue detection using hidden Markov model in real driving condition. Expert System with Application. 2016;63(C):397-411.
Lal SKL, Craig A. Driver fatigue: electroencephalography and psychological assessment. Psychophysiology. 2002;39(3):313-321.
Simon M, Schmidt EA, Kincses WE, Fritzsche M, Bruns A, Aufmuth C, Bogdan M, Rosenstiel W, Schrauf M. EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions. Clinical Neurophysiology. 2011;122(6):1168-1178.
Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology Heart & Circulatory Physiology. 2000;278(6):2039-2049.
Fu RR, Wang H. Detectio
Guest Editor: Eleonora Papadimitriou, PhD
Editors: Marko Matulin, PhD, Dario Babić, PhD, Marko Ševrović, PhD
The mission of our journal is to provide high quality communication of research results, which is crucial for the effectiveness of scientific research, the promotion of scientific information, new ideas, and original research in the field of traffic and transportation engineering.
1848-4069
0353-5320
Proud publisher
Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal
