Open AccessDiscrimination of Driver Fatigue Based on Distortion Energy Density Theory and Multiple Physiological Signals
Author(s) -
Lin Wang,
Hong Wang,
Jintao Liu
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3125052
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Driver fatigue is an important contributor to traffic accidents, and driver fatigue is significant for the safety of people’s lives. Aiming to prevent traffic accidents caused by driver fatigue, a series of real driving experiments was carried out in the present work. First, based on an analysis with respect to distortion energy density (DED) theory and the experimental results, the upper trapezius at 6th neck vertebrae is more sensitive to driver fatigue and easier to fatigue than that at 7th neck vertebrae in a real driving. And then 2 cm from the 6th vertebrae on both sides were selected as the locations of data acquisition for electromyography (EMG) signal. The experimental results show that the approximate entropy (ApEn) from the electroencephalography (EEG), EMG, and respiration (RESP) signals decreases with increasing driving time, indicating that the degree of fatigue increases. After approximately 90 min, the rate of decrease in ApEn becomes slow, indicating deeper driver fatigue. According to three-D analysis, principal component analysis, and fuzzy C-means clustering analysis, the EEG-EMG combination effectively reflects the state of drivers. Finally, the ApEns from EEG and EMG were selected as independent variables, and a discriminant model of driver fatigue based on Mahalanobis distance theory was built. The accuracy of the model is up to 90.92% by 10-fold cross validation. The reasons for the high accuracy are the reasonable selection of the locations of EMG data acquisition and better degree of discrimination of EEG and EMG. The main contributions of this study are to provide a theoretical foundation for establishing internationally recognized standard locations for neck EMG data acquisition, and to provide a feasible method for discriminating driver fatigue in real driving tasks.