Human-Centered Multi-Sensor Framework for Identifying Driving Patterns Associated with Cognitive Decline Through Quantitative Analysis

Human-Centered Multi-Sensor Framework for Identifying Driving Patterns Associated with Cognitive Decline Through Quantitative Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report Human-Centered Multi-Sensor Framework for Identifying Driving Patterns Associated with Cognitive Decline Through Quantitative Analysis Sonia Moshfeghi, Seyedeh Gol Ara Ghoreishi, Muhammad Tanveer Jan, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9131446/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Driving places high demands on cognition, making everyday driving behavior a promising domain for detecting Mild Cognitive Impairment (MCI). This pilot proof-of-concept study deployed AutoPi telematics units in 51 older drivers, including 10 with MCI and 41 who were cognitively unimpaired, and monitored them over 28 months. The resulting dataset comprised 20,145 trips captured through GPS, IMU, and OBD-II sensor streams. We developed a multi-stage analytical framework that combined K-means clustering for behavioral profiling, Random Forest for feature ranking, Welch’s t-tests with Benjamini-Hochberg correction, and L1-regularized logistic regression with participant-level leave-one-out cross-validation. The model achieved an AUC of 0.698 (95% CI: 0.493–0.872) and a sensitivity of 0.800. Throttle position variability and mean throttle application emerged as the strongest sensor-derived predictors, each with a Cohen’s d of 0.86, suggesting impaired speed regulation that is consistent with executive dysfunction in MCI. However, the cohort was notably imbalanced by gender, with 9 of the 10 participants in the MCI group being female, indicating that demographic characteristics, especially gender, contributed substantially to the model’s overall discrimination. When gender was excluded, performance declined to an AUC of 0.598, which was nearly identical to the telematics-only result of 0.595. This finding suggests that the driving-behavior signal is meaningful, but modest, once demographic confounding is removed. A cold-start analysis further showed that approximately 50 trips, corresponding to about four months of naturalistic driving, may represent the minimum observation window needed for reliable screening. Subgroup analyses indicated that observed performance disparities were more likely driven by cohort composition than by systematic model bias. Overall, these findings support telematics-based MCI monitoring as a promising direction, while also highlighting the need for validation in larger and more gender-balanced cohorts before clinical deployment. Telematics systems Cognitive impairment Driving behavior Quantitative analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 03 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviews received at journal 01 May, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 03 Apr, 2026 Submission checks completed at journal 21 Mar, 2026 First submitted to journal 15 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9131446","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":620263856,"identity":"91e98e50-aa3d-49f8-89b7-bddda5a01fc1","order_by":0,"name":"Sonia Moshfeghi","email":"","orcid":"","institution":"Florida Atlantic University","correspondingAuthor":false,"prefix":"","firstName":"Sonia","middleName":"","lastName":"Moshfeghi","suffix":""},{"id":620263857,"identity":"db0c1ef9-8ef6-43ab-8cc8-6dcc1cc44da3","order_by":1,"name":"Seyedeh Gol Ara Ghoreishi","email":"","orcid":"","institution":"Florida Atlantic 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This pilot proof-of-concept study deployed AutoPi telematics units in 51 older drivers, including 10 with MCI and 41 who were cognitively unimpaired, and monitored them over 28 months. The resulting dataset comprised 20,145 trips captured through GPS, IMU, and OBD-II sensor streams. We developed a multi-stage analytical framework that combined K-means clustering for behavioral profiling, Random Forest for feature ranking, Welch’s t-tests with Benjamini-Hochberg correction, and L1-regularized logistic regression with participant-level leave-one-out cross-validation. The model achieved an AUC of 0.698 (95% CI: 0.493–0.872) and a sensitivity of 0.800. Throttle position variability and mean throttle application emerged as the strongest sensor-derived predictors, each with a Cohen’s d of 0.86, suggesting impaired speed regulation that is consistent with executive dysfunction in MCI. 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