Automatic drowsiness detection for safety-critical operations using ensemble models and EEG signals

Ramos, Plìnio M. S.; Maior, Caio Bezerra Souto; Moura, Márcio das Chagas; Lins, Isis Didier

doi:10.1016/j.psep.2022.06.039

Cited by 25 publications

(4 citation statements)

References 58 publications

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“…psychological feature recognition has a broad application prospect, and it can be potentially applied to the field of education. [18].…”

Section: Introductionmentioning

confidence: 99%

A Method of Extracting and Identifying College Students’ Music Psychological Features Based on EEG Signals

Liang¹

2022

Scientific Programming

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With the development of information technology, music education in universities is also changing. Traditional music education can not effectively explore the feature of students, resulting in the quality of music education being restricted. The rapid development of Electroencephalogram (EEG) signals has brought a new educational model to music education. Through the extraction of students’ psychological features of music by EEG, psychological features can be identified and different educational programs can be formulated according to the results. Multifeature extraction and combination method can improve the accuracy of EEG feature extraction. Using empirical mode decomposition and wavelet packet decomposition of the two kinds of methods to analyze EEG data, respectively, then the average energy, volatility index, sample entropy, and approximate entropy and multiscale features such as permutation entropy and Hurst index, select features in combination, to classify the feature set after the combination, so as to find out the feature of the performance of the optimal combination. The experimental results show that the feature combination of sample entropy and approximate entropy can better represent the main features of EEG psychological characteristic signals after wavelet packet decomposition, and the recognition accuracy is more than 90%.

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“…psychological feature recognition has a broad application prospect, and it can be potentially applied to the field of education. [18].…”

Section: Introductionmentioning

confidence: 99%

A Method of Extracting and Identifying College Students’ Music Psychological Features Based on EEG Signals

Liang¹

2022

Scientific Programming

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“…Plinio M.S. Ramos et al [24] focused on automatic sleepiness detection using a set of ML models (KNN, SVM, Random Forest (RF) and Multilayer Perceptron (MLP)) with five EEG channels. Utilizing Hjorth parameters (complexity and mobility) extracted from 14 subjects sourced from the DROZY database, their MLP classifier attained 90% accuracy in the intra mode using a single C4 electrode.…”

Section: Literaturementioning

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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“…However, this study had the drawback of a relatively small number of participants and was only conducted on a 90 km two-lane highway in Sweden. In the domain of drowsiness detection research [8], an Electroencephalography-based (EEG) drowsiness detection system using various machine learning algorithms such as Support Vector Machine with Radial Basis Function (SVM-RBF), K-Nearest Neighbors (KNN), and Random Forest (RF) was developed, but it has drawbacks. This research includes a small sample size because it only involved twelve subjects.…”

Section: Introductionmentioning

confidence: 99%

IoT-based Heart Signal Processing System for Driver Drowsiness Detection

Yunidar,

Melinda,

Khairani

et al. 2023

Green Intell. Syst. Appl.

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Traffic accidents often result in loss of life and significant economic losses. Indonesia's high number of traffic accidents indicates the need for effective solutions to overcome this problem. Developing a drowsiness detection device is one effort that can be made to reduce accidents caused by drowsy drivers. The data obtained in this study used driver heart rate data. The drowsiness detection tool was developed using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor. Testing was carried out on 25 subjects under two conditions: 'Drowsy' and 'Normal.' The driver's level of drowsiness is determined based on the heart rate measured by the detection device. The Blynk application is used as a visual interface to provide notifications via smartphone if the driver is drowsy. The accuracy of the drowsiness detection tool was compared with the results obtained from the Pulse Oximeter. This research shows that the drowsiness detection tool using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor has an accuracy of around 98% when compared with the pulse oximeter. The Blynk application successfully sends notifications precisely when the driver is drowsy. This study highlights the potential of drowsiness detection devices to improve traffic safety and reduce accidents caused by drowsy drivers.

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Automatic drowsiness detection for safety-critical operations using ensemble models and EEG signals

Cited by 25 publications

References 58 publications

A Method of Extracting and Identifying College Students’ Music Psychological Features Based on EEG Signals

A Method of Extracting and Identifying College Students’ Music Psychological Features Based on EEG Signals

Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes

IoT-based Heart Signal Processing System for Driver Drowsiness Detection

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