Smartphone-based microkinematic feature analysis for mental fatigue detection using machine learning

Smartphone-based microkinematic feature analysis for mental fatigue detection using machine learning | 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 Research Article Smartphone-based microkinematic feature analysis for mental fatigue detection using machine learning Elli Valla, Lilian Väli, Sven Nõmm, Aaro Toomela This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6601096/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Oct, 2025 Read the published version in Cognition, Technology & Work → Version 1 posted 9 You are reading this latest preprint version Abstract Mental fatigue impairs cognitive performance and productivity, posing risks in domains such as healthcare, education, and workplace safety. There is a growing need for objective, accessible tools to detect fatigue in real time. Leveraging the widespread availability and sensing capabilities of smartphones, this study presents a cross-platform system (iOS and Android) that detects mental fatigue using fine motor skill tests and a self-report questionnaire. Data were collected from 347 sessions across 166 smartphones, where participants completed tasks before and after cognitively demanding activities. From the raw motion and touch data, 60 features were engineered. Feature selection was performed using a wrapper-based method, and six machine learning algorithms were evaluated: Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and AdaBoost. Models were validated using a nested k-fold cross-validation strategy. The best-performing model achieved a sensitivity of 0.86 by combining self-reported fatigue indicators (e.g., anxiety and effort levels) with microkinematic features related to handwriting and hand tremor. This multi-dimensional approach demonstrates the feasibility of using smartphone-based motion analysis for fatigue classification. The system offers potential applications in remote health monitoring, educational assessment, and occupational safety. Additionally, the publicly available dataset provides a valuable resource for further research on cognitive and motor function assessment using mobile devices. fatigue machine learning fine motor skills microkinematics smartphone application eHealth dataset Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Oct, 2025 Read the published version in Cognition, Technology & Work → Version 1 posted Editorial decision: Revision requested 05 Jun, 2025 Reviews received at journal 03 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviewers agreed at journal 24 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers invited by journal 07 May, 2025 Editor assigned by journal 06 May, 2025 Submission checks completed at journal 06 May, 2025 First submitted to journal 06 May, 2025 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. 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. 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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-6601096","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454404032,"identity":"e0f24f55-6923-4cce-b09e-68f3609716be","order_by":0,"name":"Elli Valla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIie2RMarCQBCGd1nYNIPbrijxChPs37tKQloFy4CFCQFf4wECXuJ5g0gKGw9gYREbK4tYKZjCialsVsHGYr9mZouPf2aHMYvlG3FEnFPpd5igOqFWv1IEfyggmQxihviewloFvPcUJXiS8/kPSGd7Ka9Yu2oZ87IyKN2UBuPzECSMV8kCcaj3ufAyg4JFs8tW0GDjVQyIwb/2ZQ8Mym+rzECq0yGpEWekOLfalPL4sagAqUc8pRQfKUWY1tdNih9tSDl6yz4OvWwXpN2FQVF/aVFVOHWVCsvzKXIHKgvX1dUU0+A/P9tLWSwWi+UD7jN+RtrCkXscAAAAAElFTkSuQmCC","orcid":"","institution":"Tallinn University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Elli","middleName":"","lastName":"Valla","suffix":""},{"id":454404033,"identity":"89f5e587-d620-4ded-b8ab-f4d46516c154","order_by":1,"name":"Lilian Väli","email":"","orcid":"","institution":"Tallinn University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Lilian","middleName":"","lastName":"Väli","suffix":""},{"id":454404034,"identity":"f79a0647-81f2-4a6f-afb8-00903e3d06d3","order_by":2,"name":"Sven Nõmm","email":"","orcid":"","institution":"Tallinn University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sven","middleName":"","lastName":"Nõmm","suffix":""},{"id":454404035,"identity":"8246152f-7f86-4549-9208-5bac82bc3ea6","order_by":3,"name":"Aaro Toomela","email":"","orcid":"","institution":"Tallinn University","correspondingAuthor":false,"prefix":"","firstName":"Aaro","middleName":"","lastName":"Toomela","suffix":""}],"badges":[],"createdAt":"2025-05-06 09:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6601096/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6601096/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10111-025-00829-2","type":"published","date":"2025-10-27T15:57:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95039851,"identity":"45b46c9c-8cc4-4cf4-9c51-9a653487ef6d","added_by":"auto","created_at":"2025-11-03 16:04:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1224433,"visible":true,"origin":"","legend":"","description":"","filename":"Smartphonebasedmicrokinematicfeatureanalysisformentalfatiguedetectionusingmachinelearning2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6601096/v1_covered_54b84937-b5bc-474c-99e1-3f8666067ad1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Smartphone-based microkinematic feature analysis for mental fatigue detection using machine learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cognition-technology-and-work","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctwo","sideBox":"Learn more about [Cognition, Technology \u0026 Work](http://link.springer.com/journal/10111)","snPcode":"10111","submissionUrl":"https://submission.nature.com/new-submission/10111/3","title":"Cognition, Technology \u0026 Work","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"fatigue, machine learning, fine motor skills, microkinematics, smartphone application, eHealth, dataset","lastPublishedDoi":"10.21203/rs.3.rs-6601096/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6601096/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMental fatigue impairs cognitive performance and productivity, posing risks in domains such as healthcare, education, and workplace safety. 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