A Computational Pipeline for Activity PredictionUsing Wearable Sensor Data

A Computational Pipeline for Activity PredictionUsing Wearable Sensor Data | 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 A Computational Pipeline for Activity PredictionUsing Wearable Sensor Data Joshua Chuah, Laia Vancells-Lopez, Amy K. Loya, Danushka Bandara, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9359112/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Wearable sensors enable collection of ground reaction force (GRF) data in real-world settings, but translating these data into meaningful activity classifications remains challenging, particularly for subject-specific monitoring. Results: We present a dataset of wearable GRF measurements from 14 subjects walking across 18 combinations of speed and incline, along with a machine learning pipeline for step-level classification of loading behaviors. Continuous GRF signals were segmented into individual gait cycles and transformed into features using TSFRESH, followed by feature selection and Random Forest classification. Subject-specific models achieved a mean Top-1 accuracy of 0.664 (SD = 0.053), exceeding chance performance (0.056), with Top-2 and Top-3 accuracies of 0.836 and 0.904, respectively. Accuracy remained similar for incline-only classification (0.688 ± 0.030) but increased for speed-only classification (0.903 ± 0.097). Conclusions: These results demonstrate that step-level GRF data can support accurate classification of locomotion-related loading conditions and enable the development of subject-specific models for monitoring individual activity. The dataset and pipeline provide a foundation for future work in wearable biomechanics and personalized analysis of musculoskeletal loading. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing wearable sensor biomechanics machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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-9359112","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":625493923,"identity":"28cb2086-b254-4a40-abf2-5f5eae01d5a3","order_by":0,"name":"Joshua Chuah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYDACCQYGZgYeGxAJAsxEa0kjWQvDYRiXCC38s3sMPxfInE/czs587AFDhXViA0FL7pwxlp7BcztxZzNbugHDmXTCWgwkcgykeYBaNhzmMZNgbDtMlBbj3zw856Ba/hGnxQxoywGolgYitEjcSCuz5uFJNt5wmC1NIuFYujFBLfwzkjff5u2xk91w/vAxiQ811rIEtYABYw+UkUCUcjD4QbzSUTAKRsEoGIEAABQ1N5/LHLVgAAAAAElFTkSuQmCC","orcid":"","institution":"Union College","correspondingAuthor":true,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Chuah","suffix":""},{"id":625493924,"identity":"bf2db7e0-49c2-46bb-91c0-e443b2733c56","order_by":1,"name":"Laia Vancells-Lopez","email":"","orcid":"","institution":"Purdue University West Lafayette","correspondingAuthor":false,"prefix":"","firstName":"Laia","middleName":"","lastName":"Vancells-Lopez","suffix":""},{"id":625493925,"identity":"e2107f49-a741-4747-811f-384cd9cea87c","order_by":2,"name":"Amy K. 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Drazan","email":"","orcid":"","institution":"Fairfield University","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"F.","lastName":"Drazan","suffix":""}],"badges":[],"createdAt":"2026-04-08 15:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9359112/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9359112/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108671414,"identity":"cb1134ff-63ba-481d-be5e-f72a76597bf1","added_by":"auto","created_at":"2026-05-07 07:42:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":295821,"visible":true,"origin":"","legend":"","description":"","filename":"BioinformaticsDrazan.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9359112/v1_covered_f80339ff-dd0b-4416-9ed9-56e3dd9a6241.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Computational Pipeline for Activity PredictionUsing Wearable Sensor Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"wearable sensor, biomechanics, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-9359112/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9359112/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eWearable sensors enable collection of ground reaction force (GRF) data in real-world settings, but translating these data into meaningful activity classifications remains challenging, particularly for subject-specific monitoring.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eWe present a dataset of wearable GRF measurements from 14 subjects walking across 18 combinations of speed and incline, along with a machine learning pipeline for step-level classification of loading behaviors. Continuous GRF signals were segmented into individual gait cycles and transformed into features using TSFRESH, followed by feature selection and Random Forest classification. Subject-specific models achieved a mean Top-1 accuracy of 0.664 (SD = 0.053), exceeding chance performance (0.056), with Top-2 and Top-3 accuracies of 0.836 and 0.904, respectively. Accuracy remained similar for incline-only classification (0.688 \u0026plusmn; 0.030) but increased for speed-only classification (0.903 \u0026plusmn; 0.097).\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eThese results demonstrate that step-level GRF data can support accurate classification of locomotion-related loading conditions and enable the development of subject-specific models for monitoring individual activity. 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