Driver drowsiness detection using ANN image processing

Vesselényi, Tiberiu; Moca, Sorin; Rus, Alexandru; Mitran, Tudor; Tataru, Bogdan

doi:10.1088/1757-899x/252/1/012097

Cited by 49 publications

(27 citation statements)

References 7 publications

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“…Table 2. Performance comparison with respect to such similar methods described in Reference [30]. These findings prove that the present research is very promising in identifying a methodology characterized by minimum invasiveness, high reliability, and considerable speed in the response, which allows for the identification of the reduction in the level of vigilance of a person within a few seconds.…”

Section: Results Of the Softmax Blocksupporting

confidence: 59%

“…The results seem very good and will be used as a benchmark for the algorithm proposed herein. In Reference [30], the authors proposed a study regarding the possibility to develop a drowsiness detection system for car drivers based on the integration of three methods: EEG, EOG signal processing, and driver image analysis. The approach seems very promising, but it needs two-dimensional images of the driver during the car driving, so the complexity of the pipeline is greater than the same ones based on EEG processing only.…”

Section: Introductionmentioning

confidence: 99%

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An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal

et al. 2019

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The development of detection methodologies for reliable drowsiness tracking is a challenging task requiring both appropriate signal inputs and accurate and robust algorithms of analysis. The aim of this research is to develop an advanced method to detect the drowsiness stage in electroencephalogram (EEG), the most reliable physiological measurement, using the promising Machine Learning methodologies. The methods used in this paper are based on Machine Learning methodologies such as stacked autoencoder with softmax layers. Results obtained from 62 volunteers indicate 100% accuracy in drowsy/wakeful discrimination, proving that this approach can be very promising for use in the next generation of medical devices. This methodology can be extended to other uses in everyday life in which the maintaining of the level of vigilance is critical. Future works aim to perform extended validation of the proposed pipeline with a wide-range training set in which we integrate the photoplethysmogram (PPG) signal and visual information with EEG analysis in order to improve the robustness of the overall approach.

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Section: Results Of the Softmax Blocksupporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal

et al. 2019

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“…These methods evaluate mainly three parameters: eye movements (eye blinking and eye closure activity) via eye-tracking, that was also investigated for usage in maritime operations and aviation [19][20][21], facial expressions (yawning, jaw drop, brow rise, and lip stretch), and head position (head scaling/nodding) [22]. In particular, many studies focused on the use of machine (deep) learning-based approaches [23][24][25][26][27]. Apart from research, numerous commercial products are available that rely on behavioral measures for drowsiness detection.…”

Section: Driver Drowsiness Measurement Technologiesmentioning

confidence: 99%

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Kundinger

Sofra

Riener

2020

Sensors

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Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.

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“…Interestingly, DL networks offer great potential for biomedical signals analysis through the simplification of raw input signals (i.e., through various steps including feature extraction, denoising, and feature selection) and the improvement of the classification results. Various DL models have been applied to biomedical signal analysis [ 36 ] particularly for recurrent neural networks (RNNs) [ 37 ], long short-term memory (LSTM) [ 38 ], auto-encoder (AE) [ 39 ], convolutional neural networks (CNNs) [ 40 ], deep stacking networks (DSNs) [ 41 ], etc. Among them, CNNs models [ 42 ] are the most frequently used in biomedical signals classification for anomaly detection due to its high classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

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Driver drowsiness detection using ANN image processing

Cited by 49 publications

References 7 publications

An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal

An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

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