A Multi-Modal Driver Fatigue and Distraction Assessment System

Craye, Céline; Rashwan, Abdullah; Kamel, Mohamed S.; Karray, Fakhri

doi:10.1007/s13177-015-0112-9

Cited by 62 publications

(48 citation statements)

References 34 publications

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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%

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

confidence: 93%

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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“…The results proved that ANFIS has more accurate prediction capability. Driver performance along with heart rate and behavioral attribute data were combined for the DD and fatigue detection in [35]. Classification was performed by a multi-modal approach based on HMM, SVM, and BN.…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Aksjonov

Nedoma

Vodovozov

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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In addition to vehicle control, drivers often perform secondary tasks that impede driving. Reduction of driver distraction is an important challenge for the safety of intelligent transportation systems. In this paper, a methodology for detection and evaluation of driver distraction while performing secondary tasks is described and an appropriate hardware and software environment is offered and studied. The system includes a model of normal driving, a subsystem for measuring the errors from the secondary tasks, and a module for total distraction evaluation. A new machine learning algorithm defines driver performance in lane keeping and speed maintenance on a specific road segment. To recognize the errors, a method is proposed, which compares normal driving parameters with ones obtained while conducting a secondary task. To evaluate distraction, an effective fuzzy logic algorithm is used. To verify the proposed approach, a case study with driver-in-the-loop experiments was carried out, in which participants performed the secondary task, namely chatting on a cell phone. The results presented in this research confirm its capability to detect and to precisely measure a level of abnormal driver performance.

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“…Driving measures Vehicle control signals also encode changes in driving performance that reflect distraction [18]. They serve as complementary information to other modalities.…”

Section: Acoustics -Speechmentioning

confidence: 99%

“…Finally, a simple and effective multimodal fusion technique using a polynomial fusion layer shows superior distraction detection results compared to the baseline SVM and neural network models. Index Terms: multimodal interaction, speech processing, spoken dialog systemsCombinations of modalities There have been attempts to use multimodal fusion for distraction detection [16,15,18,13,19].…”

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confidence: 99%

Multimodal Polynomial Fusion for Detecting Driver Distraction

Du¹,

Raman²,

Black³

et al. 2018

Interspeech 2018

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Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone. Although there has been a considerable amount of research on modeling the distracted behavior of drivers under various conditions, accurate automatic detection using multiple modalities and especially the contribution of using the speech modality to improve accuracy has received little attention. This paper introduces a new multimodal dataset for distracted driving behavior and discusses automatic distraction detection using features from three modalities: facial expression, speech and car signals. Detailed multimodal feature analysis shows that adding more modalities monotonically increases the predictive accuracy of the model. Finally, a simple and effective multimodal fusion technique using a polynomial fusion layer shows superior distraction detection results compared to the baseline SVM and neural network models. Index Terms: multimodal interaction, speech processing, spoken dialog systemsCombinations of modalities There have been attempts to use multimodal fusion for distraction detection [16,15,18,13,19].

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A Multi-Modal Driver Fatigue and Distraction Assessment System

Cited by 62 publications

References 34 publications

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

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

Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Multimodal Polynomial Fusion for Detecting Driver Distraction

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