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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.05463 |
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| _version_ | 1866911435891671040 |
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| author | Hasan, Md Zahid Basulto-Elias, Guillermo Chang, Jun Ha Hallmark, Shauna Rizzo, Matthew Sharma, Anuj Sarkar, Soumik |
| author_facet | Hasan, Md Zahid Basulto-Elias, Guillermo Chang, Jun Ha Hallmark, Shauna Rizzo, Matthew Sharma, Anuj Sarkar, Soumik |
| contents | We introduce scenario-based cognitive status identification in older drivers from naturalistic driving videos, leveraging large vision models. In recent times, cognitive decline including Dementia and Mild Cognitive Impairment (MCI), is often underdiagnosed due to the time-consuming and costly nature of current diagnostic methods. By analyzing real-world driving behavior captured through in-vehicle sensors, this study aims to extract "digital fingerprints" that correlate with functional decline and clinical features of dementia. Moreover, modern large vision models can draw meaningful insights from everyday driving patterns across different roadway scenarios to early detect cognitive decline. We propose a framework that uses large vision models and naturalistic driving videos to analyze driver behavior, identify cognitive status and predict disease progression. We leverage the strong relationship between real-world driving behavior as an observation of the current cognitive status of the drivers where the vehicle can be utilized as a "diagnostic tool". Our method identifies early warning signs of functional impairment, contributing to proactive intervention strategies. This work enhances early detection and supports the development of scalable, non-invasive monitoring systems to mitigate the growing societal and economic burden of cognitive decline in the aging population. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05463 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Driving as a Diagnostic Tool: Scenario-based Cognitive Assessment in Older Drivers from Driving Video Hasan, Md Zahid Basulto-Elias, Guillermo Chang, Jun Ha Hallmark, Shauna Rizzo, Matthew Sharma, Anuj Sarkar, Soumik Computer Vision and Pattern Recognition Artificial Intelligence We introduce scenario-based cognitive status identification in older drivers from naturalistic driving videos, leveraging large vision models. In recent times, cognitive decline including Dementia and Mild Cognitive Impairment (MCI), is often underdiagnosed due to the time-consuming and costly nature of current diagnostic methods. By analyzing real-world driving behavior captured through in-vehicle sensors, this study aims to extract "digital fingerprints" that correlate with functional decline and clinical features of dementia. Moreover, modern large vision models can draw meaningful insights from everyday driving patterns across different roadway scenarios to early detect cognitive decline. We propose a framework that uses large vision models and naturalistic driving videos to analyze driver behavior, identify cognitive status and predict disease progression. We leverage the strong relationship between real-world driving behavior as an observation of the current cognitive status of the drivers where the vehicle can be utilized as a "diagnostic tool". Our method identifies early warning signs of functional impairment, contributing to proactive intervention strategies. This work enhances early detection and supports the development of scalable, non-invasive monitoring systems to mitigate the growing societal and economic burden of cognitive decline in the aging population. |
| title | Driving as a Diagnostic Tool: Scenario-based Cognitive Assessment in Older Drivers from Driving Video |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2507.05463 |