Classifying Interpersonal Synchrony in VR Joint Actions: Human-Human versus Human-Bot Interactions

Classifying Interpersonal Synchrony in VR Joint Actions: Human-Human versus Human-Bot Interactions | 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 Classifying Interpersonal Synchrony in VR Joint Actions: Human-Human versus Human-Bot Interactions Yoshiko ARIMA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5846894/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 This study collected sensor data from VR goggles in pairs and examined interpersonal synchrony using a within-subject design. Participants positioned opposite each other in VR space judged object categories (kitchen vs. garage utensils) and responded with movements. We created an LSTM classification model using movement data from pairs instructed to interact either competitively (Game session) or synchronously (Collab session). The model achieved 93.2% accuracy (F1 score = 0.93.2) in classifying these interaction modes using only three-axis head acceleration data from the goggles. We then applied this within-subject trained model to Bot interaction sessions, where participants worked with Bot avatars under high or low accuracy conditions. Dynamic Time Warping (DTW) distance was used to quantify synchrony. Results showed that when movements were classified as synchronous, DTW distance was lower, indicating higher synchrony. A significant interaction effect was found between movement classification (Game/Collab) and Bot accuracy condition on DTW distance. Exploratory cross-subject validation showed reduced performance (F1 = 0.72), though head rotation features remained important across participants. These findings demonstrate that the classification model successfully captures interaction dynamics across different task conditions within subjects, and that synchrony with Bots is modulated by behavioral predictability. VR sensor data joint action interpersonal synchrony human activity recognition machine learning classification Full Text Additional Declarations No competing interests reported. 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-5846894","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[{"code":1,"date":"2025-06-11 18:46:15","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research 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Interactions","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":true,"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":"VR sensor data, joint action, interpersonal synchrony, human activity recognition, machine learning classification","lastPublishedDoi":"10.21203/rs.3.rs-5846894/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5846894/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study collected sensor data from VR goggles in pairs and examined interpersonal synchrony using a within-subject design. Participants positioned opposite each other in VR space judged object categories (kitchen vs. garage utensils) and responded with movements. We created an LSTM classification model using movement data from pairs instructed to interact either competitively (Game session) or synchronously (Collab session). The model achieved 93.2% accuracy (F1 score\u0026thinsp;=\u0026thinsp;0.93.2) in classifying these interaction modes using only three-axis head acceleration data from the goggles. We then applied this within-subject trained model to Bot interaction sessions, where participants worked with Bot avatars under high or low accuracy conditions. Dynamic Time Warping (DTW) distance was used to quantify synchrony. Results showed that when movements were classified as synchronous, DTW distance was lower, indicating higher synchrony. A significant interaction effect was found between movement classification (Game/Collab) and Bot accuracy condition on DTW distance. Exploratory cross-subject validation showed reduced performance (F1\u0026thinsp;=\u0026thinsp;0.72), though head rotation features remained important across participants. 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