A systematic review of physiological signals based driver drowsiness detection systems

Saleem, Adil Ali; Siddiqui, Hafeez Ur Rehman; Raza, Muhammad Amjad; Rustam, Furqan; Dudley, Sandra; Ashraf, Imran

doi:10.1007/s11571-022-09898-9

Cited by 14 publications

(5 citation statements)

References 96 publications

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“…Prior to leveraging EEG in studying diminished alertness, a series of treatments must be undertaken to extract relevant EEG characteristics, thereby facilitating drowsiness detection [28]. Figure 1 illustrates the typical EEG signal processing chain employed for drowsiness detection.…”

Section: Eeg-based Drowsiness Detectionmentioning

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: Eeg-based Drowsiness Detectionmentioning

confidence: 99%

Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes

Zayed,

Belhadj,

Ben Khalifa

et al. 2024

Preprint

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“…Driver distraction signifies reduced focus on one's actions, which can be serious for protective driving without challenging activities. There are various factors that distract drivers' attention; however, in practice, only two major types have been studied: (1) fatigue and (2) distraction [3]. The term fatigue describes the integration of signs that diminish performance and a subjective sense of tiredness.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Road Safety: Deep Learning-Based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems

Yang,

2024

Electronics

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Driver drowsiness detection is a significant element of Advanced Driver-Assistance Systems (ADASs), which utilize deep learning (DL) methods to improve road safety. A driver drowsiness detection system can trigger timely alerts like auditory or visual warnings, thereby stimulating drivers to take corrective measures and ultimately avoiding possible accidents caused by impaired driving. This study presents a Deep Learning-based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems (DLID3-ADAS) technique. The DLID3-ADAS technique aims to enhance road safety via the detection of drowsiness among drivers. Using the DLID3-ADAS technique, complex features from images are derived through the use of the ShuffleNet approach. Moreover, the Northern Goshawk Optimization (NGO) algorithm is exploited for the selection of optimum hyperparameters for the ShuffleNet model. Lastly, an extreme learning machine (ELM) model is used to properly detect and classify the drowsiness states of drivers. The extensive set of experiments conducted based on the Yawdd driver database showed that the DLID3-ADAS technique achieves a higher performance compared to existing models, with a maximum accuracy of 97.05% and minimum computational time of 0.60 s.

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“…Drowsiness detection systems can be classified into three main categories: vehicle dynamics, physiological signals, and recognition of driver face characteristics [ 11 , 12 , 15 , 16 ]. Nevertheless, the efficacy of vehicle dynamics-based systems is hindered by the suboptimal performance caused by unpredictable variables such as road geometry, sluggish processing speed, traffic conditions, and head movement [ 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…Night-shift male workers and individuals with sleep apnea syndrome emerge as high-risk categories [4]. Several research studies have been published, suggesting strategies to mitigate or notify drivers about possible indications of drowsiness [5][6][7][8][9][10][11][12][13][14]. These measures are important steps in tackling the critical issue of drowsy driving and improving road safety.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence

Siddiqui,

Akmal,

Iqbal

et al. 2024

Sensors

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Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.

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A systematic review of physiological signals based driver drowsiness detection systems

Cited by 14 publications

References 96 publications

Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes

Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes

Enhancing Road Safety: Deep Learning-Based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems

Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence

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