ECG-Based Driver’s Stress Detection Using Deep Transfer Learning and Fuzzy Logic Approaches

Amin, Muhammad; Ullah, Khalil; Asif, Muhammad; Waheed, Abdul; Haq, Sana ul; Zareei, Mahdi; Biswal, Rajesh Roshan

doi:10.1109/access.2022.3158658

Cited by 19 publications

(5 citation statements)

References 56 publications

(70 reference statements)

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“…Recently, Amin et al [31] developed several deep transfer learning model (GoogLeNet, DarkNet-53, ResNet-101, InceptionResNetV2, Xception, DenseNet-201, and InceptionV3) to detect the three driver's cognitive load levels (low, medium, and high) from ECG based scalogram images. They achieved overall accuracy of 98.11%.…”

Section: Resultsmentioning

confidence: 99%

A review of driver cognitive load detection using ECG signals

Tjolleng,

Dyota Pramudita

2024

SHS Web Conf.

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Detection of the driver’s cognitive load while driving is crucial to prevent the likelihood of traffic collisions and improve road safety. A physiological-based approach has gained significant attention due to its potential to provide reliable indicators for the driver’s state. The physiological signal of electrocardiography (ECG) is considered a promising biomarker for detecting the driver’s cognitive load. Despite the interest in cognitive load detection using ECG, an attempt has yet to be made to identify the relationship between ECG measures and driver cognitive load level. This paper seeks to investigate this gap in cognitive load literature. The finding demonstrates that further research is still needed on ECG-based driver’s cognitive load detection by examining and analyzing the limitations of research challenges and earlier studies. This study also addresses the performance and problems faced in the detection of a driver’s cognitive load considering ECG. With a better understanding of how cognitive load affects ECG measures, both researchers and companies can design more effective driver’s state detection systems.

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Section: Resultsmentioning

confidence: 99%

A review of driver cognitive load detection using ECG signals

Tjolleng,

Dyota Pramudita

2024

SHS Web Conf.

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“…A CNN network was used for stress classification, with decisionlevel fusion applied to three image inputs, resulting in an accuracy of 85.45%. Amin [22] conducted a study on the stress level detection of a driver to reduce the risks caused by a driver's health conditions. Hence, the Drive database-a public database provided by Physionet-was used.…”

Section: Related Studiesmentioning

confidence: 99%

Stress Classification Using ECGs Based on a Multi-Dimensional Feature Fusion of LSTM and Xception

Song,

Kim,

Kim

et al. 2024

IEEE Access

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In the information age, people are increasingly being exposed to stress as societies are experiencing sudden changes fueled by advancements in cutting-edge scientific technology and the Information Technology (IT) industry. Consequently, research has been actively conducted on stress classification to mitigate psychological and physical diseases caused by constantly feeling stressed. Specifically, the number of studies examining electrocardiograms (ECGs), which record biosignals that provide insight into the response level of the body's autonomic nervous system, has increased. However, previous studies on stress classification based on ECG used only one-dimensional feature data, thus entailing difficulties in analyzing the data more closely and comprehensively owing to bias toward a specific aspect. Therefore, to overcome the limitations of conventional stress classification based on ECGs, this paper developed a stress classification method based on multi-dimensional feature fusion of LSTM and Xception using ECGs from which outliers have been removed. Experimental results showed that applying multidimensional feature fusion of the weighted average method using ECG data with outlier signals removed resulted in a stress classification of 99.51%, a 1.25% improvement from previous studies which used only one-dimensional feature data of ECGs, thus highlighting the excellent performance of the proposed stress classification method using ECG based on multi-dimensional feature fusion of LSTM and Xception.

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“…The eight-step fuzzy EDAS procedure [65] defined in Section 3.6 is utilized here to evaluate the ranks of the SRAD, BH, E4-L, E4-R, E4-(L+R), and BH+E4-(L+R)-based CNN and hybrid CNN-LSTM models for the two-stress and three-stress classes. This procedure is separately followed for each the driver's stress level.…”

Section: Rank-based Performance Evaluationmentioning

confidence: 99%

Real-World Driver Stress Recognition and Diagnosis Based on Multimodal Deep Learning and Fuzzy EDAS Approaches

et al. 2023

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Mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in damage to humans, vehicles, and infrastructure. Likewise, persistent mental stress could lead to the development of mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning approaches. These approaches recognize different levels of stress based on handcrafted features extracted from various modalities including physiological, physical, and contextual data. Acquiring good quality features from these modalities using feature engineering is often a difficult job. Recent developments in the form of deep learning (DL) algorithms have relieved feature engineering by automatically extracting and learning resilient features. This paper proposes different CNN and CNN-LSTSM-based fusion models using physiological signals (SRAD dataset) and multimodal data (AffectiveROAD dataset) for the driver’s two and three stress levels. The fuzzy EDAS (evaluation based on distance from average solution) approach is used to evaluate the performance of the proposed models based on different classification metrics (accuracy, recall, precision, F-score, and specificity). Fuzzy EDAS performance estimation shows that the proposed CNN and hybrid CNN-LSTM models achieved the first ranks based on the fusion of BH, E4-Left (E4-L), and E4-Right (E4-R). Results showed the significance of multimodal data for designing an accurate and trustworthy stress recognition diagnosing model for real-world driving conditions. The proposed model can also be used for the diagnosis of the stress level of a subject during other daily life activities.

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ECG-Based Driver’s Stress Detection Using Deep Transfer Learning and Fuzzy Logic Approaches

Cited by 19 publications

References 56 publications

A review of driver cognitive load detection using ECG signals

A review of driver cognitive load detection using ECG signals

Stress Classification Using ECGs Based on a Multi-Dimensional Feature Fusion of LSTM and Xception

Real-World Driver Stress Recognition and Diagnosis Based on Multimodal Deep Learning and Fuzzy EDAS Approaches

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