Driver Drowsiness Monitoring System Using Fusion of Facial Features &amp; EEG

Salimuddin, Misbah Kazi; Ramarao,; Panbude, Shraddha

doi:10.1109/iccons.2018.8663138

Cited by 5 publications

(9 citation statements)

References 5 publications

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“…For the classification, the established Support Vector Machine (SVM) has been adopted in many existing works related to fatigue and drowsy recognition. [2,3] achieved 90% and 94.63% accuracy rate respectively in their application. Furthermore, [9,10,13] also adopted SVM and achieved accuracy rate more that 90%.…”

Section: Introductionmentioning

confidence: 95%

“…This is due to the increasing number of road accidents around the world. It is reported, fatigue and drowsy among drivers are the main causes of road accidents [1][2][3][4][5][6]. Fatigue and drowsy among drivers can be expressed as a condition where a driver has turn into a state of unconsciousness and it led to fall into a sleep.…”

Section: Introductionmentioning

confidence: 99%

“…There are three approaches available in measuring the fatigue and drowsiness which are biological or physical indicators, vehicle behavior and face or behavioral analysis [3,5,6]. Firstly, biological indicator uses brain waves, heart rate and pulse rate.…”

Section: Introductionmentioning

confidence: 99%

“…However, these two approaches require complex devices and can be very costly. Hence, the third approach namely face or behavioral analysis which utilize face expression from images or video offer less cost nevertheless promise good performance output [3,5].…”

Section: Introductionmentioning

confidence: 99%

“…One of the reliable features that can be used to represent the facial features are the shape features. [2,3,9] used shape feature namely HAAR, HOG and LBP respectively in extracting face features in measuring the fatigue and drowsy expression of driver. Moreover, [10][11][12] also used shape features based on Viola-Jones for extracting facial features for driver's fatigue and drowsy recognition.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Driver’s Fatigue and Drowsy Expression Recognition Using Shape Features and Support Vector Machine

Mangshor

Yazid

Ibrahim

et al. 2021

Lecture Notes in Electrical Engineering

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The task of recognition and classification of driver's fatigue and drowsy face expression is a challenging task. There are several face parts involved in determining the fatigue and drowsiness levels of the driver. The face parts involved are eyes and mouths which portray different face expressions such as mouth open when yawning and shut or open eyes. Hence, it is very important to recognize the facial expression of the drivers based on these features. Therefore, this paper proposed the use of Shape Features for extracting the face features which are left eyes, right eyes and mouth. Next, Support Vector Machine (SVM) is deployed for the purposed of classification. Experiments have been conducted on the real-time images and promising result has achieved. The average classification accuracy achieved is 83.33%. Findings from this study is believed to expand the research towards building a fatigue and drowsy model for assisting driver in reducing traffic road accident.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Driver’s Fatigue and Drowsy Expression Recognition Using Shape Features and Support Vector Machine

Mangshor

Yazid

Ibrahim

et al. 2021

Lecture Notes in Electrical Engineering

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A scoping review on the use of consumer-grade EEG devices for research

Sabio

Williams

McArthur

et al. 2022

Preprint

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BACKGROUND: Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience. PURPOSE: The aim of this study was to chart peer-review articles that used currently available consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG. METHODS: We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following electronic databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country. RESULTS: We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were most used, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common use was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes. CONCLUSIONS: Consumer-grade EEG has proven to be a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who wish to use these devices.

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Fatigue and mental underload further pronounced in L3 conditionally automated driving: Results from an EEG experiment on a test track

Figalová

Bieg

Schulz

et al. 2023

28th International Conference on Intelligent User Interfaces

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Drivers' role changes with increasing automation from the primary driver to a system supervisor. This study investigates how supervising an SAE L2 and L3 automated vehicle (AV) affects drivers' mental workload and sleepiness compared to manual driving. Using an AV prototype on a test track, the oscillatory brain activity of 23 adult participants was recorded during L2, L3, and manual driving. Results showed decreased mental workload and increased sleepiness in L3 drives compared to L2 and manual drives, indicated by self-report scales and changes in the frontal alpha and theta power spectral density. These findings suggest that fatigue and mental underload are significant issues in L3 driving and should be considered when designing future AV interfaces.

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Driver Drowsiness Monitoring System Using Fusion of Facial Features & EEG

Cited by 5 publications

References 5 publications

Driver’s Fatigue and Drowsy Expression Recognition Using Shape Features and Support Vector Machine

Driver’s Fatigue and Drowsy Expression Recognition Using Shape Features and Support Vector Machine

A scoping review on the use of consumer-grade EEG devices for research

Fatigue and mental underload further pronounced in L3 conditionally automated driving: Results from an EEG experiment on a test track

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