Identifying and Predicting Cognitive Decline Using Multi-Modal Sensor Data and Machine Learning Approach

Identifying and Predicting Cognitive Decline Using Multi-Modal Sensor Data and Machine Learning Approach | 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 Article Identifying and Predicting Cognitive Decline Using Multi-Modal Sensor Data and Machine Learning Approach Aparna Joshi, Jun Ha Chang, Guillermo Basulto-Elias, Shauna Hallmark, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6735622/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Alzheimer’s Disease (AD) remains a critical global health challenge, with its prevalence expected to rise dramatically by 2050, leading to substantial financial and emotional burdens. Mild Cognitive Impairment (MCI), the prodromal stage of AD, presents a crucial opportunity for early intervention, yet its diagnosis remains difficult due to the overlap with normal aging. Traditional diagnostic methods, such as neuroimaging and cerebrospinal fluid analysis, are costly and invasive, highlighting the need for alternative, scalable, and non-invasive biomarkers. This study explores the potential of naturalistic driving behavior as a digital biomarker for detecting cognitive decline in individuals at risk for AD and MCI. A total of 118 participants (8 with AD, 65 with MCI, and 45 cognitively healthy individuals) were included in this study. At baseline year, we measured their demographics, cognitive status administrated by dementia experts, 3 consecutive months of naturalistic driving performance and driving life-space from participants’ own vehicle and sleep data via wrist-worn actigraphy, integrated into multi-modal data to feed to XGBoost-based framework. After 1-year follow, their cognitive status was assessed. We implemented a two-phase validation framework: first, classification model using Leave-One-Subject-Out Cross-Validation (LOSO-CV) to classify baseline cognitive status, and then, conducting a prediction model with to assess the model’s ability to predict 1-year follow-up cognitive status. Our results demonstrate that the multi-modal classifier achieved strong classification performance (accuracy = 68.64%; precision = 73.97%; F1-score=74.48%), with the highest recall (76.39%) from a model incorporating demographics and driving features, and prediction performance (accuracy = 70.48%; precision = 71.88%; F1-score = 74.80%, recall = 77.97%). Key predictive features included sex, mean awakening duration, age, average acceleration, and sleep efficiency, underscoring the relevance of driving behavior and sleep characteristics in cognitive assessment. By leveraging everyday activities such as driving, this framework provides a novel, non-invasive approach for identifying individuals at risk for cognitive decline. Furthermore, its ability to predict future disease progression establishes a forward-looking paradigm for early detection and monitoring. Beyond cognitive impairment, this methodology offers a scalable and generalizable framework for disease prediction, with potential applications in detecting and monitoring other neurodegenerative and chronic conditions. Health sciences/Biomarkers Health sciences/Medical research Mild Cognitive Impairment Alzheimer's Naturalistic Driving Digital Biomarker Machine Learning Leave-One-Subject-Out Approach Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementarydocumentAJ.docx Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6735622","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":521264752,"identity":"35829a51-3170-4c5e-ac51-682fcf34f252","order_by":0,"name":"Aparna 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Mild Cognitive Impairment (MCI), the prodromal stage of AD, presents a crucial opportunity for early intervention, yet its diagnosis remains difficult due to the overlap with normal aging. Traditional diagnostic methods, such as neuroimaging and cerebrospinal fluid analysis, are costly and invasive, highlighting the need for alternative, scalable, and non-invasive biomarkers. This study explores the potential of naturalistic driving behavior as a digital biomarker for detecting cognitive decline in individuals at risk for AD and MCI. A total of 118 participants (8 with AD, 65 with MCI, and 45 cognitively healthy individuals) were included in this study. At baseline year, we measured their demographics, cognitive status administrated by dementia experts, 3 consecutive months of naturalistic driving performance and driving life-space from participants’ own vehicle and sleep data via wrist-worn actigraphy, integrated into multi-modal data to feed to XGBoost-based framework. After 1-year follow, their cognitive status was assessed.\u003cem\u003e \u003c/em\u003eWe implemented a two-phase validation framework: first, classification model using Leave-One-Subject-Out Cross-Validation (LOSO-CV) to classify baseline cognitive status, and then, conducting a prediction model with to assess the model’s ability to predict 1-year follow-up cognitive status. \u0026nbsp;Our results demonstrate that the multi-modal classifier achieved strong classification performance (accuracy = 68.64%; precision = 73.97%; F1-score=74.48%), with the highest recall (76.39%) from a model incorporating demographics and driving features, and prediction performance (accuracy = 70.48%; precision = 71.88%; F1-score = 74.80%, recall = 77.97%). Key predictive features included sex, mean awakening duration, age, average acceleration, and sleep efficiency, underscoring the relevance of driving behavior and sleep characteristics in cognitive assessment. 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