UniTF-ADA: A Unified Time-Frequency Framework with Adaptive Loss and Data Augmentations for Robust Driver Fatigue Recognition.
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| Title: | UniTF-ADA: A Unified Time-Frequency Framework with Adaptive Loss and Data Augmentations for Robust Driver Fatigue Recognition. |
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| Authors: | Liu, Yan1 2798323980@qq.com, Liu, Jie2 17359111@qq.com, Yan, Hong3 yanhong@yku.edu.cn, Zhang, Wenyu4 zhangwenyu8518@ustl.edu.cn, Liu, Xiaomeng1 1312540696@qq.com, Guo, Xu1 1114105794@qq.com |
| Source: | Engineering Letters. Jun2026, Vol. 34 Issue 6, p2401-2410. 10p. |
| Subjects: | Time-frequency analysis, Data augmentation, Loss functions (Statistics), Electrophysiology, Statistical reliability, Wakefulness |
| Abstract: | In fatigue driving detection, conventional methods based on video or behavioral features are easily disturbed by lighting, occlusion, and other environmental factors, making stable and reliable recognition difficult. In contrast, electroencephalogram (EEG) signals directly reflect the driver's neural activity and provide stronger anti-interference capability and higher temporal resolution, and thus have become an important data source for fatigue modeling. However, most existing EEGbased approaches still rely on features from a single domain and therefore lack the ability to jointly characterize rhythmic dynamics and structural rhythm changes. Meanwhile, mainstream public datasets contain relatively few samples and fail to adequately capture inter-individual variability. To address these limitations, this paper proposes a unified architecture, UniTFADA, which fuses multi-dimensional fatigue-related features via time-frequency dual-domain modeling. Considering the limited number of samples, we further design a pseudo-distribution generation strategy to expand the training variation space and improve model robustness. In addition, a dynamic weighted multi-objective loss function is introduced to adaptively balance different discrimination targets, stabilizing training and enhancing cross-subject generalization. Experimental results on two public datasets show that, compared with the Deformer baseline, UniTF-ADA improves ACC and Macro-F1 by 1.38% and 1.69% on Dataset I, and by 8.31% and 9.06% on Dataset II, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194195721 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: UniTF-ADA: A Unified Time-Frequency Framework with Adaptive Loss and Data Augmentations for Robust Driver Fatigue Recognition. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Yan%22">Liu, Yan</searchLink><relatesTo>1</relatesTo><i> 2798323980@qq.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Jie%22">Liu, Jie</searchLink><relatesTo>2</relatesTo><i> 17359111@qq.com</i><br /><searchLink fieldCode="AR" term="%22Yan%2C+Hong%22">Yan, Hong</searchLink><relatesTo>3</relatesTo><i> yanhong@yku.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Wenyu%22">Zhang, Wenyu</searchLink><relatesTo>4</relatesTo><i> zhangwenyu8518@ustl.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Xiaomeng%22">Liu, Xiaomeng</searchLink><relatesTo>1</relatesTo><i> 1312540696@qq.com</i><br /><searchLink fieldCode="AR" term="%22Guo%2C+Xu%22">Guo, Xu</searchLink><relatesTo>1</relatesTo><i> 1114105794@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Jun2026, Vol. 34 Issue 6, p2401-2410. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Time-frequency+analysis%22">Time-frequency analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Loss+functions+%28Statistics%29%22">Loss functions (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Electrophysiology%22">Electrophysiology</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+reliability%22">Statistical reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Wakefulness%22">Wakefulness</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In fatigue driving detection, conventional methods based on video or behavioral features are easily disturbed by lighting, occlusion, and other environmental factors, making stable and reliable recognition difficult. In contrast, electroencephalogram (EEG) signals directly reflect the driver's neural activity and provide stronger anti-interference capability and higher temporal resolution, and thus have become an important data source for fatigue modeling. However, most existing EEGbased approaches still rely on features from a single domain and therefore lack the ability to jointly characterize rhythmic dynamics and structural rhythm changes. Meanwhile, mainstream public datasets contain relatively few samples and fail to adequately capture inter-individual variability. To address these limitations, this paper proposes a unified architecture, UniTFADA, which fuses multi-dimensional fatigue-related features via time-frequency dual-domain modeling. Considering the limited number of samples, we further design a pseudo-distribution generation strategy to expand the training variation space and improve model robustness. In addition, a dynamic weighted multi-objective loss function is introduced to adaptively balance different discrimination targets, stabilizing training and enhancing cross-subject generalization. Experimental results on two public datasets show that, compared with the Deformer baseline, UniTF-ADA improves ACC and Macro-F1 by 1.38% and 1.69% on Dataset I, and by 8.31% and 9.06% on Dataset II, respectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 2401 Subjects: – SubjectFull: Time-frequency analysis Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: Loss functions (Statistics) Type: general – SubjectFull: Electrophysiology Type: general – SubjectFull: Statistical reliability Type: general – SubjectFull: Wakefulness Type: general Titles: – TitleFull: UniTF-ADA: A Unified Time-Frequency Framework with Adaptive Loss and Data Augmentations for Robust Driver Fatigue Recognition. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Yan – PersonEntity: Name: NameFull: Liu, Jie – PersonEntity: Name: NameFull: Yan, Hong – PersonEntity: Name: NameFull: Zhang, Wenyu – PersonEntity: Name: NameFull: Liu, Xiaomeng – PersonEntity: Name: NameFull: Guo, Xu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 6 Titles: – TitleFull: Engineering Letters Type: main |
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