Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection
Given GPS points on a transportation network, the goal of the Quad-tree Based Driver Classification (QBDC) problem is to identify whether drivers have Mild Cognitive Impairment (MCI). The QBDC problem is challenging due to the large volume and complexity of the data. This paper proposes a quad-tree...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10955159/ |
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| author | Seyedeh Gol Ara Ghoreishi Charles Boateng Sonia Moshfeghi Muhammad Tanveer Jan Joshua Conniff Kwangsoo Yang Jinwoo Jang Borko Furht David Newman Ruth Tappen Monica Rosselli Kelley L. Jackson |
| author_facet | Seyedeh Gol Ara Ghoreishi Charles Boateng Sonia Moshfeghi Muhammad Tanveer Jan Joshua Conniff Kwangsoo Yang Jinwoo Jang Borko Furht David Newman Ruth Tappen Monica Rosselli Kelley L. Jackson |
| author_sort | Seyedeh Gol Ara Ghoreishi |
| collection | DOAJ |
| description | Given GPS points on a transportation network, the goal of the Quad-tree Based Driver Classification (QBDC) problem is to identify whether drivers have Mild Cognitive Impairment (MCI). The QBDC problem is challenging due to the large volume and complexity of the data. This paper proposes a quad-tree based approach to the QBDC problem by analyzing driving patterns using a real-world dataset. We propose a geo-regional quad-tree structure to capture the spatial hierarchy of driving trajectories and introduce new driving features representation for input into a convolutional neural network (CNN) for driver classification. The experimental results demonstrate the effectiveness of the proposed algorithm, achieving an F1 score of 95% that significantly outperforms the baseline models. These results highlight the potential of geo-regional quad-tree structures to extract interpretable features and describe complex driving patterns. This approach offers significant implications for driver classification, with the potential to improve road safety and cognitive health monitoring. |
| format | Article |
| id | doaj-art-24a154aa5acf473d8fbc0e676f8b83f3 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-24a154aa5acf473d8fbc0e676f8b83f32025-08-20T02:11:37ZengIEEEIEEE Access2169-35362025-01-0113631296314210.1109/ACCESS.2025.355870610955159Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment DetectionSeyedeh Gol Ara Ghoreishi0https://orcid.org/0009-0007-3244-932XCharles Boateng1https://orcid.org/0009-0009-7930-5360Sonia Moshfeghi2Muhammad Tanveer Jan3https://orcid.org/0000-0002-3870-0526Joshua Conniff4Kwangsoo Yang5https://orcid.org/0000-0003-4293-9908Jinwoo Jang6https://orcid.org/0000-0002-3657-698XBorko Furht7David Newman8https://orcid.org/0000-0003-0423-6267Ruth Tappen9Monica Rosselli10https://orcid.org/0000-0003-2656-9505Kelley L. Jackson11College of Eng and Computer Science, Florida Atlantic University, Boca Raton, FL, USACollege of Eng and Computer Science, Florida Atlantic University, Boca Raton, FL, USACollege of Eng and Computer Science, Florida Atlantic University, Boca Raton, FL, USACollege of Eng and Computer Science, Florida Atlantic University, Boca Raton, FL, USACollege of Science, Florida Atlantic University, Boca Raton, FL, USACollege of Eng and Computer Science, Florida Atlantic University, Boca Raton, FL, USACollege of Eng and Computer Science, Florida Atlantic University, Boca Raton, FL, USACollege of Eng and Computer Science, Florida Atlantic University, Boca Raton, FL, USACollege of Nursing, Florida Atlantic University, Boca Raton, FL, USACollege of Nursing, Florida Atlantic University, Boca Raton, FL, USACollege of Science, Florida Atlantic University, Boca Raton, FL, USACollege of Nursing, Florida Atlantic University, Boca Raton, FL, USAGiven GPS points on a transportation network, the goal of the Quad-tree Based Driver Classification (QBDC) problem is to identify whether drivers have Mild Cognitive Impairment (MCI). The QBDC problem is challenging due to the large volume and complexity of the data. This paper proposes a quad-tree based approach to the QBDC problem by analyzing driving patterns using a real-world dataset. We propose a geo-regional quad-tree structure to capture the spatial hierarchy of driving trajectories and introduce new driving features representation for input into a convolutional neural network (CNN) for driver classification. The experimental results demonstrate the effectiveness of the proposed algorithm, achieving an F1 score of 95% that significantly outperforms the baseline models. These results highlight the potential of geo-regional quad-tree structures to extract interpretable features and describe complex driving patterns. This approach offers significant implications for driver classification, with the potential to improve road safety and cognitive health monitoring.https://ieeexplore.ieee.org/document/10955159/Spatiotemporal dataGPS datatrajectory analysisdriving behaviorolder driver classificationquad-tree decomposition |
| spellingShingle | Seyedeh Gol Ara Ghoreishi Charles Boateng Sonia Moshfeghi Muhammad Tanveer Jan Joshua Conniff Kwangsoo Yang Jinwoo Jang Borko Furht David Newman Ruth Tappen Monica Rosselli Kelley L. Jackson Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection IEEE Access Spatiotemporal data GPS data trajectory analysis driving behavior older driver classification quad-tree decomposition |
| title | Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection |
| title_full | Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection |
| title_fullStr | Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection |
| title_full_unstemmed | Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection |
| title_short | Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection |
| title_sort | quad tree based driver classification using deep learning for mild cognitive impairment detection |
| topic | Spatiotemporal data GPS data trajectory analysis driving behavior older driver classification quad-tree decomposition |
| url | https://ieeexplore.ieee.org/document/10955159/ |
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