Methodology and initial analysis results for development of non-invasive and hybrid driver drowsiness detection systems

Nowadays, accidents occur during drowsy road trips and increase day by day; It is a known fact that many accidents occur due to driver fatigue and sometimes inattention, this research is primarily devoted to maximizing efforts to identify drowsiness. State of the driver under real driving conditions. The aim of driver drowsiness detection systems is to try to reduce these traffic accidents. The secondary data collected focuses on previous research on systems for detecting drowsiness and several methods have been used to detect drowsiness or inattentive driving.Our goal is to provide an interface where the program can automatically detect the driver's drowsiness and detect it in the event of an accident by using the image of a person captured by the webcam and examining how this information can be used to improve driving safety can be used. . a vehicle safety project that helps prevent accidents caused by the driver's sleep. Basically, you're collecting a human image from the webcam and exploring how that information could be used to improve driving safety. Collect images from the live webcam stream and apply machine learning algorithm to the image and recognize the drowsy driver or not.When the driver is sleepy, it plays the buzzer alarm and increases the buzzer sound. If the driver doesn't wake up, they'll send a text message and email to their family members about their situation. Hence, this utility goes beyond the problem of detecting drowsiness while driving. Eye extraction, face extraction with dlib.

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A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems

Sensors ◽

10.3390/s21113786 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3786

Author(s):

Igor Stancin ◽

Mario Cifrek ◽

Alan Jovic

Keyword(s):

Deep Learning ◽

Difficult Problem ◽

Learning Approaches ◽

Eeg Signal ◽

Drowsiness Detection ◽

Detection Systems ◽

Driver Drowsiness ◽

Signal Features ◽

Challenges And Opportunities ◽

Electroencephalogram Eeg

Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.

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Non-Invasive Driver Drowsiness Detection System

Sensors ◽

10.3390/s21144833 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4833

Author(s):

Hafeez Ur Rehman Siddiqui ◽

Adil Ali Saleem ◽

Robert Brown ◽

Bahattin Bademci ◽

Ernesto Lee ◽

...

Keyword(s):

Support Vector Machine ◽

Detection System ◽

Ultra Wideband ◽

Gradient Boosting ◽

Support Vector ◽

Drowsiness Detection ◽

Non Invasive ◽

Driver Drowsiness ◽

Uwb Radar ◽

Tree Classifier

Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death. Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar. Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy). Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%. This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration.

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A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG

Methods ◽

10.1016/j.ymeth.2021.04.017 ◽

2021 ◽

Author(s):

Jian Cui ◽

Zirui Lan ◽

Yisi Liu ◽

Ruilin Li ◽

Fan Li ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Single Channel ◽

Drowsiness Detection ◽

Driver Drowsiness

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Using long short term memory and convolutional neural networks for driver drowsiness detection

Accident Analysis & Prevention ◽

10.1016/j.aap.2021.106107 ◽

2021 ◽

Vol 156 ◽

pp. 106107

Author(s):

Azhar Quddus ◽

Ali Shahidi Zandi ◽

Laura Prest ◽

Felix J.E. Comeau

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Drowsiness Detection ◽

Driver Drowsiness ◽

Long Short Term Memory

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Driver Drowsiness Estimation Based on Factorized Bilinear Feature Fusion and a Long-Short-Term Recurrent Convolutional Network

Information ◽

10.3390/info12010003 ◽

2020 ◽

Vol 12 (1) ◽

pp. 3

Author(s):

Shuang Chen ◽

Zengcai Wang ◽

Wenxin Chen

Keyword(s):

Short Term Memory ◽

Feature Fusion ◽

Detection Methods ◽

Video Frame ◽

Estimation Model ◽

Short Term ◽

Convolutional Network ◽

Drowsiness Detection ◽

Driver Drowsiness ◽

Time Information

The effective detection of driver drowsiness is an important measure to prevent traffic accidents. Most existing drowsiness detection methods only use a single facial feature to identify fatigue status, ignoring the complex correlation between fatigue features and the time information of fatigue features, and this reduces the recognition accuracy. To solve these problems, we propose a driver sleepiness estimation model based on factorized bilinear feature fusion and a long- short-term recurrent convolutional network to detect driver drowsiness efficiently and accurately. The proposed framework includes three models: fatigue feature extraction, fatigue feature fusion, and driver drowsiness detection. First, we used a convolutional neural network (CNN) to effectively extract the deep representation of eye and mouth-related fatigue features from the face area detected in each video frame. Then, based on the factorized bilinear feature fusion model, we performed a nonlinear fusion of the deep feature representations of the eyes and mouth. Finally, we input a series of fused frame-level features into a long-short-term memory (LSTM) unit to obtain the time information of the features and used the softmax classifier to detect sleepiness. The proposed framework was evaluated with the National Tsing Hua University drowsy driver detection (NTHU-DDD) video dataset. The experimental results showed that this method had better stability and robustness compared with other methods.

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