Fatigue State Detection for Tired Persons in Presence of Driving Periods

Alharbey, Riad; Dessouky, Mohamed M.; Sedik, Ahmed; Siam, Ali I.; Elaskily, Mohamed A.

doi:10.1109/access.2022.3185251

Cited by 18 publications

(6 citation statements)

References 29 publications

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“…They utilize pretrained networks like VGG16 and VGG19 to streamline computations. R. Alharbey et al [3] devised a model that integrates two distinct techniques. The first method employs a machine learning approach, utilizing EEG signals as input data.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Driver Drowsiness Detection Solution with CNN Algorithm Integration

2024

IRJMETS

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Frequent accidents occur due to drivers experiencing drowsiness, especially during nighttime journeys where fatigue can lead to unintentional sleep. To address this issue, our system employs advanced machine learning techniques, particularly Deep Learning algorithms, to accurately detect signs of driver tiredness. Unlike older methods, our system utilizes Convolutional Neural Networks (CNNs) for enhanced accuracy and adaptability across various environmental conditions. CNNs offer real-time processing, resilience to lighting changes, and the ability to capture intricate spatial features, making them superior to previous image processing techniques. Our model incorporates Haar cascade Classifier for facial feature extraction and Shi-Tomasi corner detection, as well as Lukas-Kanade optical flow algorithm for tracking head movements. With these methods, we achieved a high accuracy rate of 98%.

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Section: Related Workmentioning

confidence: 99%

Enhanced Driver Drowsiness Detection Solution with CNN Algorithm Integration

2024

IRJMETS

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“…These techniques represent a fusion of physical and behavioral strategies. For example, Ko et al [31] proposed a system that extracts Differential Entropy (DE) from EEG signals and applies CNN for classification. This process generates hierarchical features and class-discriminative information, enabling the detection of sleepiness via a densityconnected layer.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Fatigue Detection System Based on 3D CNNs and Ensemble Models

2023

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Due to the widespread issue of road accidents, researchers have been drawn to investigate strategies to prevent them. One major contributing factor to these accidents is driver fatigue resulting from exhaustion. Various approaches have been explored to address this issue, with machine and deep learning proving to be effective in processing images and videos to detect asymmetric signs of fatigue, such as yawning, facial characteristics, and eye closure. This study proposes a multistage system utilizing machine and deep learning techniques. The first stage is designed to detect asymmetric states, including tiredness and non-vigilance as well as yawning. The second stage is focused on detecting eye closure. The machine learning approach employs several algorithms, including Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Multi-layer Perceptron (MLP), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Meanwhile, the deep learning approach utilizes 2D and 3D Convolutional Neural Networks (CNNs). The architectures of proposed deep learning models are designed after several trials, and their parameters have been selected to achieve optimal performance. The effectiveness of the proposed methods is evaluated using video and image datasets, where the video dataset is classified into three states: alert, tired, and non-vigilant, while the image dataset is classified based on four facial symptoms, including open or closed eyes and yawning. A more robust system is achieved by combining the image and video datasets, resulting in multiple classes for detection. Simulation results demonstrate that the 3D CNN proposed in this study outperforms the other methods, with detection accuracies of 99 percent, 99 percent, and 98 percent for the image, video, and mixed datasets, respectively. Notably, this achievement surpasses the highest accuracy of 97 percent found in the literature, suggesting that the proposed methods for detecting drowsiness are indeed effective solutions.

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“…Artificial intelligence techniques have opened up previously unthinkable possibilities and changed innovation in a number of fields. Such as applying these techniques in Thyroid classifications [1], heart conditions detection [2], facial expression recognition [3], automatic stress detection [4], level of consciousness detection [5] and Fatigue state detection [6].…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Approach for Drowning Detection and Alertness

Ibrahim,

Ali,

Ahmed

et al. 2024

Egyptian Journal of Artificial Intelligence

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In this paper, a unique approach has been developed to detect drowning by using a chest area belt equipped with a pressure sensor to monitor the breathing rate of the person wearing the belt. Additionally, MPU6050 module has been used to measure the cumulative displacement to track the swimmer's movement. These sensors have been integrated with a Remotexy application and a GSM module to create an SMS message that alerts lifeguards when someone is in danger of drowning. The chest area belt is equipped with carbon resistors that are sensitive to pressure changes, allowing us to monitor the breathing rate of the person wearing the belt. By monitoring the breathing rate, we can detect when a swimmer is in distress and alert lifeguards to the potential drowning. To track the swimmer's movement, an MPU6050 has been used, which is a motion sensor that measures the cumulative displacement of the person wearing the belt. This data is then integrated with a Remotexy application and a GSM module to create an SMS message that is sent to lifeguards when someone is in danger of drowning.

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Fatigue State Detection for Tired Persons in Presence of Driving Periods

Cited by 18 publications

References 29 publications

Enhanced Driver Drowsiness Detection Solution with CNN Algorithm Integration

Enhanced Driver Drowsiness Detection Solution with CNN Algorithm Integration

An Adaptive Fatigue Detection System Based on 3D CNNs and Ensemble Models

A Low-Cost Approach for Drowning Detection and Alertness

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