Discussion on Machine Learning and Deep Learning based Makeup Considered Eye Status Recognition for Driver Drowsiness

Nojiri, Naoto; Kong, Xiangbo; Meng, Lin; Shimakawa, Hiromitsu

doi:10.1016/j.procs.2019.01.252

Cited by 17 publications

(7 citation statements)

References 9 publications

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“…DL is the subset of ML that allows computers to mimic the human brain to complete classification tasks on images or non-visual data sets. There are number of algorithms that are used to detect the driver drowsiness (5). It is challenging to select the appropriate algorithms that detect drowsiness with high accuracy.…”

Section: Figure I Artificial Intelligence and Its Subsetsmentioning

confidence: 99%

“…A lot of research is caried out to use the abilities of SVM algorithm in the drowsiness detection of the driver. The system achieved near 100% accuracy in face detection, but due to low frame rate, it missed the facial expressions (5). CNN (Convolutional neural network) is mostly used to detect the facial expressions and proven successful for image recognition, video analysis, and classification.…”

Section: Based Algorithms Used For Detecting Driver Drowsinessmentioning

confidence: 99%

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AI Based Novel Approach to Detect Driver Drowsiness

2022

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Vehicle accidents due to driver drowsiness is the major problem all over the world. It leads to millions of road accidents that result in fatal and non-fatal injuries and huge financial losses. To overcome vehicle accidents and provide safety to all commuters on the roads, a smart driver drowsiness detection system is required which can detect the drowsy state of the driver at an early stage. The behavioural measure provides the promising results to detect driver drowsiness at an early stage due to its non-intrusive nature. In this measure, various facial features, ML (Machine Learning) or DL (Deep Learning) based algorithms are analyzed various states of the driver to detect drowsiness. In this paper, a comparative study has been conducted that use ML and DL based algorithms to detect driver drowsiness. This study is aimed of finding effective AI (Artificial Intelligence) approach to detect driver drowsiness at an early stage.

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Section: Figure I Artificial Intelligence and Its Subsetsmentioning

confidence: 99%

Section: Based Algorithms Used For Detecting Driver Drowsinessmentioning

confidence: 99%

AI Based Novel Approach to Detect Driver Drowsiness

2022

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“…It is used to assess quality in the context of object detection and classification. One of the important contributions is that its inception modules are designed for realising dimensionality reduction (Krizhevsky et al, 2012;Szegedy et al, 2015;Nojiri et al, 2019).…”

Section: Eye Status Recognitionmentioning

confidence: 99%

A self-learning fall detection system for elderly persons using depth camera

2020

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The machine learning revolution is redesigning modern healthcare, and with the growth of the elderly population, fall detection has become an important research topic in healthcare. This paper surveys advances in machine learning-based fall detection technologies and reviews sensor-based, image processing-based, and wearable sensor-based fall detection systems and applications. In addition, this paper proposes a self-learning posture analysis and eye status-based fall detection system to solve the issue of mis-detections in fall detection systems, which have not been addressed in past works. Furthermore, this work proposes an image-feature-separation system that can use image processing with a low risk of privacy disclosure. Moreover, this work establishes a dataset, which includes 36 non-fall/fall cases comprising 25,200 images that can be used not only for this research but also in related studies. Experimental results show that this system can detect a fall with high accuracy and solve mis-detections in machine learning-based fall detection systems.

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“…Some studies also showed that the level of drowsiness in automated driving is significantly higher than in manual driving [10][11][12] . Given all this evidence, the estimation of driver fatigue is essential for road safety and also future intelligent transportation systems require a vigilant driver for take-over requests from automated vehicles failing to perform safely.Generally, three types of data have been used in the literature to design driver drowsiness detection systems: (1) vehicle-based 13,14 , (2) vision-based 15,16 , and (3) physiological data 17,18 . The literature suggests that physiological data such as EEG may be more appropriate than other systems to detect the onset of driver drowsiness 19,20 specifically because vehicle-based and vision-based systems can be too late in warning the driver in the early stages of drowsiness, when there might still be time to prevent the accident.…”

mentioning

confidence: 99%

“…Generally, three types of data have been used in the literature to design driver drowsiness detection systems: (1) vehicle-based 13,14 , (2) vision-based 15,16 , and (3) physiological data 17,18 . The literature suggests that physiological data such as EEG may be more appropriate than other systems to detect the onset of driver drowsiness 19,20 specifically because vehicle-based and vision-based systems can be too late in warning the driver in the early stages of drowsiness, when there might still be time to prevent the accident.…”

mentioning

confidence: 99%

Driver drowsiness estimation using EEG signals with a dynamical encoder–decoder modeling framework

Arefnezhad

Hamet

Eichberger

et al. 2022

Sci Rep

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Drowsiness is a leading cause of accidents on the road as it negatively affects the driver’s ability to safely operate a vehicle. Neural activity recorded by EEG electrodes is a widely used physiological correlate of driver drowsiness. This paper presents a novel dynamical modeling solution to estimate the instantaneous level of the driver drowsiness using EEG signals, where the PERcentage of eyelid CLOSure (PERCLOS) is employed as the ground truth of driver drowsiness. Applying our proposed modeling framework, we find neural features present in EEG data that encode PERCLOS. In the decoding phase, we use a Bayesian filtering solution to estimate the PERCLOS level over time. A data set that comprises 18 driving tests, conducted by 13 drivers, has been used to investigate the performance of the proposed framework. The modeling performance in estimation of PERCLOS provides robust and repeatable results in tests with manual and automated driving modes by an average RMSE of 0.117 (at a PERCLOS range of 0 to 1) and average High Probability Density percentage of 62.5%. We further hypothesized that there are biomarkers that encode the PERCLOS across different driving tests and participants. Using this solution, we identified possible biomarkers such as Theta and Delta powers. Results show that about 73% and 66% of the Theta and Delta powers which are selected as biomarkers are increasing as PERCLOS grows during the driving test. We argue that the proposed method is a robust and reliable solution to estimate drowsiness in real-time which opens the door in utilizing EEG-based measures in driver drowsiness detection systems.

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Discussion on Machine Learning and Deep Learning based Makeup Considered Eye Status Recognition for Driver Drowsiness

Cited by 17 publications

References 9 publications

AI Based Novel Approach to Detect Driver Drowsiness

AI Based Novel Approach to Detect Driver Drowsiness

A self-learning fall detection system for elderly persons using depth camera

Driver drowsiness estimation using EEG signals with a dynamical encoder–decoder modeling framework

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