Driver's Drowsiness Monitoring and Alarming Auto-System Based on EOG Signals

Hayawi, Ali Amer; Waleed, Jumana

doi:10.1109/iiceta47481.2019.9013000

Cited by 31 publications

(21 citation statements)

References 10 publications

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“…The main parameter for testing drowsiness classifier is accuracy. The following papers have the corresponding accuracies: [6]-86%, [7]-89.5%, [8]-94.31%, [10]-91.5%, [13]-90%, [17]-80%, [18]-85%, [20]-93.6%.…”

Section: Results Analysismentioning

confidence: 99%

“…Another novel approach in [9] of EEG uses principal component analysis and PCANet deep learning model for preprocessing and feature extraction. The work reported in [10] focuses on implementing automated driver drowsiness along with an alarming system using signals obtained from electrooculography. An embedded system circuit consisting of a microcontroller on Arduino was prepared for signal procurement.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Drowsiness Detection Scheme using Video Analysis

Murthy¹,

Siddineni²,

Kompella³

et al. 2022

IJCDS

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Road accidents caused due to drowsiness of the driver are quotidian. As per the World Health Organization global report, India has the highest number of road accidents, and about half or greater number are because of drowsy driving, and this has become a major issue. Real-time drowsiness detection models detect when the driver is feeling drowsy by monitoring behavioural aspects or by using physiological sensors. Though the use of bio-sensors gives more accurate results, they are intrusive and distract the driver. We have developed and implemented a behavioural-based drowsiness detection algorithm that monitors the movement of the face and closeness of eyes to detect and alert a drowsy driver. We successfully implemented our algorithm in Matlab-2020 software, where we took a live video from a webcam and processed each frame to classify it as either drowsy or not. We also tested on a dataset featuring live driving subjects and achieved 90% accuracy with 84% precision. If drowsiness is detected, a system audio alert is generated to alert the driver. In case eyes or face are not detected in a frame, we by default classified it as drowsy and produced the alert message because a false negative is more dangerous than a false positive, and thus attained a high recall of 98%.

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Section: Results Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Efficient Drowsiness Detection Scheme using Video Analysis

Murthy¹,

Siddineni²,

Kompella³

et al. 2022

IJCDS

View full text Add to dashboard Cite

show abstract

“…Vertical EOG and horizontal EOG signals have been picked from the subject for various purposes [20][21][22][23]. By picking the vertical EOG signal, a low-cost driver alertness system has been designed for preventing road accidents by quickly alerting the driver if he feels drowsiness to ensure their safety [24]. By acquiring the vertical and horizontal EOG, patients with severe motor disabilities have communicated with their surrounding people using EOG based speller [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Study of electrooculography signal acquisition sites for assistive device applications

Raj¹

2021

IJATEE

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Electrooculography (EOG) is a technique that involves the measurement of the corneo-retinal standing potential of the eye. The human eye acts as a dipole between the cornea (positive potential) and the retina (negative potential), creating an electric field around the eyeball. The resulting electric signal obtained from this field is called electrooculogram. These signals, generated by eye movements, could be measured by employing different electrode placement configurations for the acquisition. The properties of these signals change depending on the number and placement of the electrodes. The study conducted here describes EOG signal acquisition using new electrode placement configurations that employ fewer facial electrodes placed on the patient. Three pre-gelled disposable electrodes were utilized for this purpose. Only one electrode was placed on a facial location, enhancing patient comfort during the acquisition procedure. To support this study, a lowcost signal acquisition hardware was developed. Using active filtering and amplification, appropriate signal processing techniques were executed upon the horizontal EOG signal acquired to reduce noise and interference due to external conditions. Hence, this paper presents the findings of new electrode placement sites for the acquisition of EOG signals which could be used for assistive device applications while restricting the number of facial electrodes to one. Most of the studies regarding the EOG signal acquisition had been using all electrodes in the face region. In contrast, we reduced the number of electrodes in the face region, thereby providing patient comfort. The comparison was made mainly on the acquired data from these new locations to discover the configuration with the optimal signal response using data obtainedfrom seven healthy subjects. The amplitude values were being compared from the new locations with the standard acquisition sites. The findings of this study were found to have a productive result. The total gain of the system required for new electrode placement configurations was two times more than the total gain required for standard acquisition sites, and also, the amplitude was less but can be helpful for assistive device applications. The average peak to peak amplitude value of the EOG signal for the new site came around about 1.25 volt.

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“…The biological information includes electroencephalogram (EEG), functional magnetic resonance imaging (FMRI), electrocardiogram (ECG), electrooculogram (EOG), and electromyography (EMG) [16][17][18][19][20]. By comparing the various signal types, biological signals can be used for early intention detection [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Detection of Emergency Braking Intention From Soft Braking and Normal Driving Intentions Using EMG Signals

Feleke

2021

IEEE Access

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Emergency braking intention detection has a vital practical value for improving driving safety. This paper proposed an electromyography (EMG)-based method to detect emergency braking intention from soft braking and normal driving intentions. Temporal and spectral signatures of EMG signals of emergency braking, soft braking, and normal driving intentions were investigated. Common spatial pattern (CSP) was used to generate virtual channels. The power spectrum density and linear envelope amplitude of EMG signals were used as features, respectively. Chi-square test (Chi) was used to select features. Regularized linear discrimination analysis was developed to detect emergency braking intention from the other two driving intentions. Experiment results showed significant differences in temporal and spectral domains between three kinds of driving. Furthermore, on average, the proposed method based on spectral features can detect emergency braking intention 155.70 ms before behavior under emergency situations with a system accuracy of 95.72%. The proposed method based on EMG signals for predicting emergency braking intention can be applied to develop active driving assistance systems and improve driving safety. INDEX TERMS Detection, emergency braking, electromyography (EMG).

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Driver's Drowsiness Monitoring and Alarming Auto-System Based on EOG Signals

Cited by 31 publications

References 10 publications

An Efficient Drowsiness Detection Scheme using Video Analysis

An Efficient Drowsiness Detection Scheme using Video Analysis

Study of electrooculography signal acquisition sites for assistive device applications

Detection of Emergency Braking Intention From Soft Braking and Normal Driving Intentions Using EMG Signals

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