EOG based vigilance monitoring system

Suman, D.; Malini, M.; Anchuri, Sarath

doi:10.1109/indicon.2015.7443210

Cited by 12 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, fatigue driving monitoring technology is the artificial construction of some algorithms, through the computer ability to automatically process the monitored data, extraction and classification, and automatic classification. Then the function of high precision fatigue monitoring can be realized [74][75][76][77]. Combining multiple non-invasive fatigue methods would provide good reliability [78].…”

Section: Feature Classificationmentioning

confidence: 99%

Fatigue driving detection based on electrooculography: a review

Tian

Cao

2021

J Image Video Proc.

View full text Add to dashboard Cite

To accurately identify fatigued driving, establishing a monitoring system is one of the important guarantees of improving traffic safety and reducing traffic accidents. Among many research methods, electrooculogram signal (EOG) has unique advantages. This paper presents a systematic literature review of these technologies and summarizes a basic framework of fatigue driving monitoring system based on EOGs. Then we summarize the advantages and disadvantages of existing technologies. In addition, 80 primary references published during the last decade were identified. The multi-feature fusion technique based on EOGs performs better than other traditional methods due to its low cost, low power consumption and low intrusion, while its application is still limited which needs more efforts to obtain good and generalizable results. And then, an overview of the literature on technology is given, revealing a premier and unbiased survey of the existing empirical research of classification techniques that have been applied to fatigue driving analysis. Finally, this paper adds value to the current literature by investigating the application of EOG signals in fatigued driving and the design of related systems, future guidelines have been provided to practitioners and researchers to grasp the major contributions and challenges in the state-of-the-art research.

show abstract

Section: Feature Classificationmentioning

confidence: 99%

Fatigue driving detection based on electrooculography: a review

Tian

Cao

2021

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…Fundamentally, the actual state of the human body is usually determined by placing electrodes or bio-sensors on the body itself. However, several previous studies have reported another approach for detecting drowsiness that involves using human biological signals, such as eye movements and eye blinking obtained using an electrooculogram (EOG) [7,8], heartbeats using an electrocardiogram (ECG) [9][10][11], brain activity using an electroencephalogram (EEG) [12][13][14], monitoring muscle activity using an electromyogram (EMG) [15,16], and also pulse rate activity [17]. However, skin contact by electrodes or a bio-sensor could cause driver discomfort during driving.…”

Section: Introductionmentioning

confidence: 99%

Gazing as actual parameter for drowsiness assessment in driving simulators

Rumagit

Akbar

Utsunomiya

et al. 2019

IJEECS

View full text Add to dashboard Cite

Many traffic accidents are due to drowsy driving. However, to date, only a few studies have been conducted on the gazing properties related to drowsiness. This study was conducted with the objective of estimating the relationship between gazing properties and drowsiness in three facial expression evaluation (FEE) categories: alert (FEE = 0), lightly drowsy (FEE = 1−2), heavily drowsy (FEE = 3−4). Drowsiness was investigated based on these eye-gazing properties by analyzing the gazing signal utilizing an eye gaze tracker and FEE in a driving simulator environment. The results obtained indicate that gazing properties have significant differences among the three drowsiness conditions, with p < 0.001 in a Kruskal–Wallis test. Furthermore, the overall classification accuracy of the three drowsiness conditions based on gazing properties using a support vector machine was 76.3%. This indicates that our proposed gazing properties can be used to quantitatively assess drowsiness.

show abstract

“…For example, some of them focused on the detection of the driver lane changing intention, 6 speed/acceleration intention, 10,11 and/or brake/accelerate intention. 8,12 Some developed DBMs focused on the prediction of some physiological or mental states such as driver vigilance, 7,13,14 fatigue, [15][16][17][18] drowsiness, [19][20][21][22] aggressiveness, 23 or driver motivation. 5 Developed DBMs consider different factors that can affect the driver behavior.…”

Section: Introductionmentioning

confidence: 99%

A new framework for the computer modelling and simulation of car driver behavior

Elkosantini

Darmoul

2018

SIMULATION

View full text Add to dashboard Cite

In recent years, the simulation of personal car driver behavior has attracted increasing attention in recent research works. Such works are based on models and systems derived from social and psychological studies. The complexity of the simulation of such systems is due to the need for modeling driver behavior and the integration of psychological and physiological factors that can affect driver performance. Although there is only a limited number of models that have been proposed to simulate driver behavior, most of them suffer from limitations pertaining to the integration of some factors, an inadequacy that will be discussed in this paper. This investigation work focuses on the development of a new model for driver behavior simulation based on recent physiological and psychological theories. The model aims to reproduce the driver behavior with respect to some psychological factors. An experimental framework is also presented to build the simulation model. This article concludes by describing some examples of use or application of the suggested model.

show abstract

EOG based vigilance monitoring system

Cited by 12 publications

References 6 publications

Fatigue driving detection based on electrooculography: a review

Fatigue driving detection based on electrooculography: a review

Gazing as actual parameter for drowsiness assessment in driving simulators

A new framework for the computer modelling and simulation of car driver behavior

Contact Info

Product

Resources

About