Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features

Albadawi, Yaman; Redhaei, Aneesa Al; Takruri, Maen

doi:10.3390/jimaging9050091

Cited by 28 publications

(5 citation statements)

References 38 publications

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“…These coefficients represent details (capturing high-frequency components) and approximation (capturing low-frequency components), respectively. For each coefficient type, energy (9), entropy (10), standard deviation (11), and mean (12) will be calculated.…”

Section: Discrete Wavelet Transformationmentioning

confidence: 99%

Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes

Zayed,

Belhadj,

Ben Khalifa

et al. 2024

Preprint

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Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an Electroencephalography (EEG) based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM) the K Nearest Neighbor (KNN) the Naive Bayes (NB) the Decision Tree (DT) and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake, and drowsy). Segmentation into 10-second intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.

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Section: Discrete Wavelet Transformationmentioning

confidence: 99%

Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes

Zayed,

Belhadj,

Ben Khalifa

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The term drowsiness is directly linked with the state of being sleepy and can be stated as a subset of the term fatigueness of the driver. Albadawi et al [50] have used machine learning in order to detect the state of driver's drowsiness by extracting features associated with head pose, EAR, and MAR. Adoption of hybrid machine learning is witnessed in work of Altameem et al [51] where skin segmentation and face detection have been carried out followed by eye monitoring.…”

Section: Existing Studies Towards Driver's Drowsiness Detectionmentioning

confidence: 99%

Scaling effectivity in manifold methodologies to detect driver’s fatigueness and drowsiness state

Shankara Chari,

Prashant

2024

IJ-AI

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The state of fatigueness and drowsiness relates to the stressed physical and mental condition of a driver that reduces the ability of a driver to drive safely leading to fatal consequences of road accidents. With a rising concerns about the road safety, the premium and modern vehicles are coming up with a sophisticated technology to detect and rise alarm during the positive case of fatigueness and drowsiness. Irrespective of availability of archives of literatures towards solving this problem, it is quite unclear about the strength and weakness of varied methodologies. Therefore, this paper presents a crisp and insightful discussion about the recent methodologies associated with detecting driver's attention, fatigueness, drowsiness along with highlights of commercial devices to realize various limiting factors and constraints associated with them. The paper contributes to introduce a well-structured flow of research trend to realize various patterns of current trend adopted towards solving varied problems and significant research gaps have been identified in this process. The outcome of this paper presents that still there is an open scope of an improvement towards accomplishing the agenda towards safer driving.

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“…To solve this issue, researchers offer a lightweight CNN architecture optimised for real-time processing, allowing www.ijacsa.thesai.org for efficient and accurate drowsiness detection while conserving computational resources. The method's usefulness is demonstrated by its performance on a large dataset, where it achieves excellent accuracy in real-time sleepiness detection while overcoming the computational complexity limits of existing approaches [20].…”

Section: Related Workmentioning

confidence: 99%

COOT-Optimized Real-Time Drowsiness Detection using GRU and Enhanced Deep Belief Networks for Advanced Driver Safety

Devi,

Al-Tmimi,

Ghadir

et al. 2024

IJACSA

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Drowsiness among drivers is a major hazard to road safety, resulting in innumerable incidents globally. Despite substantial study, existing approaches for detecting drowsiness in real time continue to confront obstacles, such as low accuracy and efficiency. In these circumstances, this study tackles the critical problems of identifying drowsiness and driver safety by suggesting a novel approach that leverages the combined effectiveness of Gated Recurrent Units (GRU) and Enhanced Deep Belief Networks (EDBN), which is optimised using COOT, a new bird collective-behavioral-based optimisation algorithm. The study begins by emphasising the relevance of sleepiness detection in improving driver safety and the limitations of prior studies in reaching high accuracy in real-time detection. The suggested method tries to close this gap by combining the GRU and EDBN simulations, which are known for their temporal modelling and feature learning capabilities, respectively, to give a comprehensive solution for sleepiness detection. Following thorough experimentation, the suggested technique achieves an outstanding accuracy of around 99%, indicating its efficiency in detecting sleepiness states in real-time driving scenarios. The relevance of this research stems from its potential to greatly reduce the number of accidents caused by drowsy driving, hence improving overall road safety. Furthermore, the use of COOT to optimize the parameters of the GRU and EDBN models adds a new dimension to the research, demonstrating the effectiveness of nature-inspired optimization methodologies for improving the performance of machine learning algorithms for critical applications such as driver safety.

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Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features

Cited by 28 publications

References 38 publications

Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes

Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes

Scaling effectivity in manifold methodologies to detect driver’s fatigueness and drowsiness state

COOT-Optimized Real-Time Drowsiness Detection using GRU and Enhanced Deep Belief Networks for Advanced Driver Safety

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