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http://bura.brunel.ac.uk/handle/2438/27615
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DC Field | Value | Language |
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dc.contributor.author | Peivandi, M | - |
dc.contributor.author | Ardabili, SZ | - |
dc.contributor.author | Sheykhivand, S | - |
dc.contributor.author | Danishvar, S | - |
dc.date.accessioned | 2023-11-13T09:50:47Z | - |
dc.date.available | 2023-11-13T09:50:47Z | - |
dc.date.issued | 2023-09-29 | - |
dc.identifier | ORCID iD: Sobhan Sheykhivand https://orcid.org/0000-0002-2275-8133 | - |
dc.identifier | ORCID iD: Sebelan Danishvar https://orcid.org/0000-0002-8258-0437 | - |
dc.identifier | 8171 | - |
dc.identifier.citation | Peivandi, M. et al. (2023) 'Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach', Sensors, 23 (19), 8171, pp. 1 - 23. doi: 10.3390/s23198171. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/27615 | - |
dc.description | Data Availability Statement: Tabriz University’s ethics committee in Tabriz, Iran. Data access is private and not publicly available. | en_US |
dc.description.abstract | Copyright © 2023 by the authors. A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A multi-level driver tiredness diagnostic database based on physiological signals including ECG, EEG, EMG, and respiratory effort was developed for this aim. The EEG signal was used for processing and other recorded signals were used to confirm the driver’s fatigue so that fatigue was not confirmed based on self-report questionnaires. A customized architecture based on adversarial generative networks and convolutional neural networks (end-to-end) was utilized to select/extract features and classify different levels of fatigue. In the customized architecture, with the objective of eliminating uncertainty, type 2 fuzzy sets were used instead of activation functions such as Relu and Leaky Relu, and the performance of each was investigated. The final accuracy obtained in the three scenarios considered, two-level, three-level, and five-level, were 96.8%, 95.1%, and 89.1%, respectively. Given the suggested model’s optimal performance, which can identify five various levels of driver fatigue with high accuracy, it can be employed in practical applications of driver fatigue to warn drivers. | en_US |
dc.description.sponsorship | This research received no external funding. | en_US |
dc.format.extent | 1 - 23 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | deep learning | en_US |
dc.subject | feature extraction | en_US |
dc.subject | GAN | en_US |
dc.subject | CNN | en_US |
dc.subject | EEG | en_US |
dc.subject | physiological signals | en_US |
dc.subject | machine learning | en_US |
dc.subject | driver fatigue | en_US |
dc.title | Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/s23198171 | - |
dc.relation.isPartOf | Sensors | - |
pubs.issue | 19 | - |
pubs.publication-status | Published | - |
pubs.volume | 23 | - |
dc.identifier.eissn | 1424-8220 | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Dept of Civil and Environmental Engineering Research Papers |
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FullText.pdf | Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | 8.39 MB | Adobe PDF | View/Open |
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