EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learning
Alzheimer’s Disease is the most prevalent neurodegenerative disease, and is a leading cause of disability among the elderly. Eye movement behaviour demonstrates potential as a non-invasive biomarker for Alzheimer’s Disease, with changes detectable at an early stage after initial onset. This paper in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037024000679 |
| _version_ | 1797210067900039168 |
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| author | Gabriella Miles Melvyn Smith Nancy Zook Wenhao Zhang |
| author_facet | Gabriella Miles Melvyn Smith Nancy Zook Wenhao Zhang |
| author_sort | Gabriella Miles |
| collection | DOAJ |
| description | Alzheimer’s Disease is the most prevalent neurodegenerative disease, and is a leading cause of disability among the elderly. Eye movement behaviour demonstrates potential as a non-invasive biomarker for Alzheimer’s Disease, with changes detectable at an early stage after initial onset. This paper introduces a new publicly available dataset: EM-COGLOAD (available at https://osf.io/zjtdq/, DOI: 10.17605/OSF.IO/ZJTDQ). A dual-task paradigm was used to create effects of declined cognitive performance in 75 healthy adults as they carried out visual tracking tasks. Their eye movement was recorded, and time series classification of the extracted eye movement traces was explored using a range of deep learning techniques. The results of this showed that convolutional neural networks were able to achieve an accuracy of 87.5% when distinguishing between eye movement under low and high cognitive load, and 76% when distinguishing between the oldest and youngest age groups. |
| first_indexed | 2024-04-24T10:04:42Z |
| format | Article |
| id | doaj.art-edb2560dcd0846698e75ab817bc8e357 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-24T10:04:42Z |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-edb2560dcd0846698e75ab817bc8e3572024-04-13T04:21:11ZengElsevierComputational and Structural Biotechnology Journal2001-03702024-12-0124264280EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learningGabriella Miles0Melvyn Smith1Nancy Zook2Wenhao Zhang3Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T Block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK; Corresponding author.Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T Block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UKFaculty of Health and Applied Sciences, University of the West of England, Bristol BS16 1QY, UKCentre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T Block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UKAlzheimer’s Disease is the most prevalent neurodegenerative disease, and is a leading cause of disability among the elderly. Eye movement behaviour demonstrates potential as a non-invasive biomarker for Alzheimer’s Disease, with changes detectable at an early stage after initial onset. This paper introduces a new publicly available dataset: EM-COGLOAD (available at https://osf.io/zjtdq/, DOI: 10.17605/OSF.IO/ZJTDQ). A dual-task paradigm was used to create effects of declined cognitive performance in 75 healthy adults as they carried out visual tracking tasks. Their eye movement was recorded, and time series classification of the extracted eye movement traces was explored using a range of deep learning techniques. The results of this showed that convolutional neural networks were able to achieve an accuracy of 87.5% when distinguishing between eye movement under low and high cognitive load, and 76% when distinguishing between the oldest and youngest age groups.http://www.sciencedirect.com/science/article/pii/S2001037024000679Time series classificationEye movementDeep learningCognitive loadAge |
| spellingShingle | Gabriella Miles Melvyn Smith Nancy Zook Wenhao Zhang EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learning Computational and Structural Biotechnology Journal Time series classification Eye movement Deep learning Cognitive load Age |
| title | EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learning |
| title_full | EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learning |
| title_fullStr | EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learning |
| title_full_unstemmed | EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learning |
| title_short | EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learning |
| title_sort | em cogload an investigation into age and cognitive load detection using eye tracking and deep learning |
| topic | Time series classification Eye movement Deep learning Cognitive load Age |
| url | http://www.sciencedirect.com/science/article/pii/S2001037024000679 |
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