An Effective Hybrid Model for EEG-Based Drowsiness Detection

Budak, Ümit; Bajaj, Varun; Akbulut, Yaman; Atila, Orhan; Şengür, Abdulkadir

doi:10.1109/jsen.2019.2917850

Cited by 125 publications

(76 citation statements)

References 24 publications

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“…Typical algorithms for driver drowsiness recognition were based on three types of inputs: (i) the biometric-signal-based approach [16][17][18][19]; (ii) the vehicle-based approach [20][21][22][23] and (iii) the image-based approach [24][25][26][27]. Approach (i) is intrusive whereas approaches (ii) and (iii) are non-intrusive.…”

Section: Existing Work Of Driver Drowsiness Recognitionmentioning

confidence: 99%

“…The feature vector was formulated by cross-correlation coefficient between ECG signals and the classification problem was modeled by support vector machine (SVM). Another biometric-signal-based approach includes the electroencephalogram (EEG) signal, examples can be referred to [18] and [19] for the SVM model and long short-term memory (LSTM) model respectively.…”

Section: Existing Work Of Driver Drowsiness Recognitionmentioning

confidence: 99%

“…Various methods have been proposed for driver drowsiness recognition [16][17][18][19][20][21][22][23][24][25][26][27] and driver stress recognition [28][29][30][31][32][33][34][35]. Nonetheless, there exist several challenges and limitations that require further research.…”

Section: Research Gaps and Motivationmentioning

confidence: 99%

“…First, the comparison was carried out on driver drowsiness recognition. Table 2 summarizes the performance between proposed MOGA optimized D-MKL-SVM and existing works [16][17][18][19][20][21][22][23][24][25][26][27]. It summarizes the category of input signals, dataset, methodology, types of cross-validation and performance.…”

Section: Comparisons To Related Workmentioning

confidence: 99%

“…Therefore, EEG and video-based approaches experience challenges in signal acquisition. As a result, the performance in related works [18,19,[24][25][26][27] is reduced in a practical situation, which the methodology is governed by a maximum accuracy of 85% and 59% for an EEG-based and image-based approach.…”

Section: Comparisons To Related Workmentioning

confidence: 99%

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A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM

Chui

Lytras

Liu

2020

Sensors

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Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in full swing to develop various algorithms for both drowsiness and stress recognition. In contrast to existing works, this paper proposes a generic model using multiple-objective genetic algorithm optimized deep multiple kernel learning support vector machine that is capable to recognize both driver drowsiness and stress. This algorithm simplifies the research formulations and model complexity that one model fits two applications. Results reveal that the proposed algorithm achieves an average sensitivity of 99%, specificity of 98.3% and area under the receiver operating characteristic curve (AUC) of 97.1% for driver drowsiness recognition. For driver stress recognition, the best performance is yielded with average sensitivity of 98.7%, specificity of 98.4% and AUC of 96.9%. Analysis also indicates that the proposed algorithm using multiple-objective genetic algorithm has better performance compared to the grid search method. Multiple kernel learning enhances the performance significantly compared to single typical kernel. Compared with existing works, the proposed algorithm not only achieves higher accuracy but also addressing the typical issues of dataset in simulated environment, no cross-validation and unreliable measurement stability of input signals. Driver drowsiness is about the sleepiness of drivers that is an undesired condition in real-world driving. Surveys revealed that more than half of adult drivers felt sleepy while driving and even more severe that about 30% of them fell asleep [3]. This high prevalence can further estimate that drivers could experience accidents (affect or being influenced attributable to drowsy driving). It is worth mentioning that driver drowsiness is differed from driver fatigue [4]. Drivers drive unconsciously in the former condition but are with a conscious status in latter condition. In reality, people are usually fatigued in today's fast paced world.Stress is body's way to react any kind of threat, challenge and demand. Research has revealed that stress has played a crucial role in adapting to driving and making decision [5]. The primary sources for driver stress are congestion and adverse driving condition as well as time pressure [6]. The stress can lead to poor and dangerous driving behaviors, for instance, flashing high beams, eliciting anger in drivers, road rage and aggressive driving, which are major causes of road traffic accidents [7]. Various recent studies have been carried out on the investigation between psychological factors and driving behaviors. In [8], results revealed that anger leads to stress, which is reflecting in the form of aggressive and negative cognitive driving behavior. On the other hand, research argued that risky drivers generally exhibited more antisocial and substance misuse, reward sensitive personality features and sensation seeking [9]. In addition, traffic penalties reported by public transp...

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Section: Existing Work Of Driver Drowsiness Recognitionmentioning

confidence: 99%

Section: Existing Work Of Driver Drowsiness Recognitionmentioning

confidence: 99%

Section: Research Gaps and Motivationmentioning

confidence: 99%

Section: Comparisons To Related Workmentioning

confidence: 99%

Section: Comparisons To Related Workmentioning

confidence: 99%

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A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM

Chui

Lytras

Liu

2020

Sensors

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Using a novel convolutional neural network for plant pests detection and disease classification

et al. 2023

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Background Early plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security. Methodology An enhanced convolutional neural network (CNN) along with long short‐term memory (LSTM) using a majority voting ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre‐trained models, deep feature extractions have been obtained from connected layers. Deep features have been extracted and are sent to the LSTM layer to build a robust, enhanced LSTM‐CNN model for detecting plant pests and diseases. Experiments were carried out using a Turkey dataset, with 4447 apple pests and diseases categorized into 15 different classes. Results The study was evaluated in different CNNs using logistic regression (LR), LSTM, and extreme learning machine (ELM), focusing on plant disease detection problems. The ensemble majority voting classifier was used at the LSTM layer to detect and classify plant disease labels. Furthermore, an autonomous selection of the optimal LSTM layer network parameters was applied. Finally, the performance was validated based on sensitivity, F1 score, accuracy, and specificity using LSTM, ELM, and LR classifiers. Conclusion The presented model attained 99.2% accuracy compared to the cutting‐edge models on different classifiers such as LSTM, LR, and ELM, and performed better compared to transfer learning. Pre‐trained models, such as VGG19, VGG18, and AlexNet, demonstrated better accuracy when the fc6 layer was compared with other layers. © 2023 Society of Chemical Industry.

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Tracking of Driver Behaviour and Drowsiness in ADAS

Evstafev

Shavetov

2023

Cyber-Physical Systems and Control II

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An Effective Hybrid Model for EEG-Based Drowsiness Detection

Cited by 125 publications

References 24 publications

A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM

A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM

Using a novel convolutional neural network for plant pests detection and disease classification

Tracking of Driver Behaviour and Drowsiness in ADAS

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