Abstract
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.
Highlights
Driver fatigue is an important contributor to traffic accidents
The studies on driver fatigue detection can be divided into four categories: (1) those based on subjective questionnaires and evaluations, e.g., SOFI-25 (Swedish Occupational Fatigue Inventory-25), Karolinksa sleepiness scale (KSS) (Karolinska Sleepiness Scale), PFC (Pearson Fatigue Coefficient), SSS (Stanford Sleeping Scale) [2], [3]; (2) those based on the driver behavior, e.g., blink, pupil change, yawning, nodding and other facial features [4]; (3) those based on the vehicle behavior, e.g., steering wheel control, speed variation and lateral displacement of vehicle, stepping on the accelerator and braking [5]-[7]; and (4) those based on physiological signals, e.g., electroencephalography (EEG), electromyography (EMG), electrocardiography (ECG), electrooculography (EOG), respiration signals (RESP), etc [8]-[31]
Based on (1) the approximate entropy (ApEn) values in Figure 5, (2) the subjective feedback in the questionnaire from participants in Figure 6, and (3) the literature reviews in [52] and [53], the period from 0~30 min during a real driving is defined as the alert state, and that from 90~120 min is defined as the fatigue state in the present work
Summary
Driver fatigue is an important contributor to traffic accidents. More and more attention has been paid to the discrimination and prevention of driver fatigue, which is significant for the safety of people’s lives [1]. Fu et al [15] proposed a dynamic fatigue detection model based on the combination of EMG and other signals. Zou et al [21] recorded the RESP, ECG, and other physiological signals in a driving simulator and analyzed the physiological data; they proposed a comprehensive fatigue indicator that was used to evaluate driver fatigue. The main contributions and originality of this study included the following: (1) based on a wireless body area network (WBAN) [32], real-time, portable, wearable sampling electrodes were used to record physiological signals in real driving tasks. The main contributions of this study are to provide a theoretical foundation for determining internationally recognized standard locations for neck EMG data acquisition, and to provide a feasible method for discriminating driver fatigue in a real driving task
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