Vigilance Estimation Using a Wearable EOG Device in Real Driving Environment

Zheng, Wei‐Long; Gao, Kunpeng; Li, Gang; Liu, Wei; Liu, Chao; Liu, Jingquan; Wang, Guoxing; Lu, Bao-Liang

doi:10.1109/tits.2018.2889962

Cited by 83 publications

(37 citation statements)

References 68 publications

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“…The most commonly investigated physiological-based methods for driver drowsiness detection [22] utilize information based on brain activity (electroencephalography (EEG)) [44][45][46], cardiac activity (electrocardiography (ECG)) [47][48][49], ocular activity (electrooculography (EOG)) [50][51][52], and muscle tone (electromyography (EMG)) [53,54]. Physiological measures are reliable, accurate, and show high potential in differing wakefulness and sleep during driving [5,55].…”

Section: Driver Drowsiness Measurement Technologiesmentioning

confidence: 99%

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Kundinger

Sofra

Riener

2020

Sensors

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Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.

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Section: Driver Drowsiness Measurement Technologiesmentioning

confidence: 99%

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Kundinger

Sofra

Riener

2020

Sensors

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“…It was suggested that lower parietal alpha demonstrates attentional demands while temporal and parietal beta activities show more differential hemispheric activities during emotional and cognitive processes, respectively [37]. The heat maps demonstrate that lower beta-1 band (12-16 Hz) is indeed more similar to the lower frequencies in being associated with slower responses while lower beta-2 and mid-beta bands (16)(17)(18)(19)(20)(21)(22)(23)(24) Hz) are correlates of improved and consistent performance as well as faster RT in Go trials. We also observed that higher levels of lower-beta (12-20 Hz) from parieto-occipital channels during the EO recordings were correlated with more omission errors.…”

Section: Opposite Roles Of Beta Sub-bands In Predicting Task Consimentioning

confidence: 93%

“…In more objective assessments, the average number of errors gives a continuous measure for classification of vigilance patterns [14]. A number of other studies on fatigue and vigilance recognition use a fusion of EEG and electrooculogram (EOG), increase in the eye closure intervals (PERCLOS), variations in the circadian rhythm, slowness of reaction time, and changes in the speech signal features, off-road gaze, and face orientation [15]- [19]. Processing these physiological events requires extra modules and may not be realistic in all settings.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Reaction Time and Vigilance Variability From Spatio-Spectral Features of Resting-State EEG in a Long Sustained Attention Task

Torkamani-Azar

Kanik

Aydın

et al. 2020

IEEE J. Biomed. Health Inform.

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Resting-state brain networks represent the intrinsic state of the brain during the majority of cognitive and sensorimotor tasks. However, no study has yet presented concise predictors of task-induced vigilance variability from spectrospatial features of the pre-task, resting-state electroencephalograms (EEG). We asked ten healthy volunteers (6 females, 4 males) aged from 22 to 45.5 to participate in 105-minute fixed-sequence-varying-duration sessions of sustained attention to response task (SART). A novel and adaptive vigilance scoring scheme was designed based on the performance and response time in consecutive trials, and demonstrated large inter-participant variability in terms of maintaining consistent tonic performance. Multiple linear regression using feature relevance analysis obtained significant predictors of the mean cumulative vigilance score (CVS), mean response time, and variabilities of these scores from the resting-state, bandpower ratios of EEG signals, p<0. Brodmanns areas 35 and 36). Higher ratios of parietal alpha (8-12 Hz) from the Brodmann's areas 18, 19, and 37 during the eyes-open states predicted slower responses but more consistent CVS and reactions associated with the superior ability in vigilance maintenance. The proposed framework and these first findings on the most stable and significant attention predictors from the intrinsic EEG power ratios can be used to model attention variations during the calibration sessions of BCI applications and vigilance monitoring systems. Single-layer neural networks trained with cross-validation also captured different associations for the beta sub-bands. Increase in the gamma (28-48 Hz) and upper beta (24-28 Hz) ratios from the left central and temporal regions predicted slower reactions and more inconsistent vigilance as explained by the increased activation of default mode network (DMN) and differences between the high-and low-attention networks at temporal regions (

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“…Moreover, such systems fail to recognize aggressive driving in the form of quick and irregular turns and frequent braking while the vehicle is used on a mountain road and other winding-type roads. In Bio-signal-based methods [24], [25], the problem is that expensive sensors are used and the attachment of sensors may cause discomfort. In the single camera based solutions [26], measurement becomes difficult at night or in tunnels.…”

Section: Introductionmentioning

confidence: 99%

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

2020

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Human drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing the face of driver and vehicle dynamics via image and video processing but the traditional methods are not capable of capturing complex temporal features of driving behaviors. However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network algorithms. In this paper, we contribute to first classify and discuss Human Driver Inattentive Driving Behavior (HIDB) into two major categories, Driver Distraction (DD), Driver Fatigue (DF), or Drowsiness (DFD). Then we discuss the causes and effects of another human risky driving behavior called Aggressive Driving behavior (ADB). Aggressive driving Behavior (ADB) is a broad group of dangerous and aggressive driving styles that lead to severe accidents. Human abnormal driving behaviors DD, DFD, and ADB are affected by various factors including driver experience/inexperience of driving, age, and gender or illness. The study of the effects of these factors that may lead to deterioration in the driving skills and performance of a human driver is out of the scope of this paper. After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver. We attempt to achieve a comprehensive understanding of HIADB detection by presenting a detailed comparative analysis of all the recent techniques. Moreover, we highlight the fundamental requirements. Finally, we present and discuss some significant and essential open research challenges as future directions.

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Vigilance Estimation Using a Wearable EOG Device in Real Driving Environment

Cited by 83 publications

References 68 publications

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Prediction of Reaction Time and Vigilance Variability From Spatio-Spectral Features of Resting-State EEG in a Long Sustained Attention Task

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

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