Automated Detection of Quiet Eye Durations in Archery Using Electrooculography and Comparative Deep Learning Models

Automated Detection of Quiet Eye Durations in Archery Using Electrooculography and Comparative Deep Learning Models | 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 Automated Detection of Quiet Eye Durations in Archery Using Electrooculography and Comparative Deep Learning Models Fatma Söğüt, Hüseyin Yanık, Evren Değirmenci, İnci Kesilmiş, Ülkü Çömelekoğlu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6586053/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract This study presents a deep learning-based approach for the automated detection of Quiet Eye (QE) durations from electrooculography (EOG) signals in archery. QE—the final fixation or tracking of the gaze before executing a motor action—is a critical factor in precision sports. Traditional detection methods, which rely on expert evaluations, are inherently subjective, time-consuming, and inconsistent. To overcome these limitations, EOG data were collected from 10 licensed archers during controlled shooting sessions and preprocessed using a wavelet transform and a Butterworth bandpass filter for noise reduction. We implemented and compared five deep learning models—CNN + LSTM, CNN + GRU, Transformer, UNet, and 1D CNN—for QE detection. The CNN + LSTM model achieved the highest accuracy (95%), followed closely by CNN + GRU (93%), demonstrating superior performance in capturing both spatial and temporal dependencies in the EOG signals. Although Transformer-based and UNet models performed competitively, they exhibited lower precision in distinguishing QE periods. These results indicate that deep learning provides an effective and scalable solution for objective QE analysis, substantially reducing the dependence on expert annotations. This automated approach can enhance sports training by offering real-time, data-driven feedback to athletes and coaches. Furthermore, the methodology holds promise for broader applications in cognitive and motor skill assessments across various domains. Future work will focus on expanding the dataset, enabling real-time deployment, and evaluating model generalizability across different skill levels and sports disciplines. Quiet eye Electrooculography Wavelet Transform Convolutional Neural Networks Long-Short Term Memory Transformer UNet GRU Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviews received at journal 10 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviewers agreed at journal 07 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers invited by journal 05 Jun, 2025 Editor assigned by journal 04 Jun, 2025 Editor invited by journal 12 May, 2025 Submission checks completed at journal 10 May, 2025 First submitted to journal 10 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. 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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-6586053","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467873084,"identity":"b80540d3-8179-47f8-b141-dd11b872c3a3","order_by":0,"name":"Fatma Söğüt","email":"","orcid":"","institution":"Mersin University","correspondingAuthor":false,"prefix":"","firstName":"Fatma","middleName":"","lastName":"Söğüt","suffix":""},{"id":467873085,"identity":"87045a30-d9ef-4444-900e-d7ef221b314a","order_by":1,"name":"Hüseyin Yanık","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYBACAwTJ2MzwAUQzgwg2IrUwzkDWwoNXCwQwMyNU4dFiLpH+8DFPwZ3E/tnNzca2OTb58u28Bxg+lB1msJc+gFWL5YwcY2Meg2eJM+4cbE7O3ZZmueEwXwLjjHOHGXj4ErA77EYOmzSPweHEhhuJzYdztx02MGDmMWDmbQNqweEygxvpz3+DtMwHabHc9t9Avhmo5S9eLQlmzCAtG4Bakhm3HTBgOAzUwohPy5k3xpJzDA4bbwRqMezdlmxgANRysOdcOg/PGRxajqc//PDmz2HZeTfSH0v83GZnIN9/xvDBjzJrOfYe7FpAgAnoAscGZJEDDLijBQwYfzAw2ONTMApGwSgYBSMcAACkO12AY/sEvwAAAABJRU5ErkJggg==","orcid":"","institution":"Mersin University","correspondingAuthor":true,"prefix":"","firstName":"Hüseyin","middleName":"","lastName":"Yanık","suffix":""},{"id":467873086,"identity":"4c0b042b-18e4-4ce5-a784-773fc36ddb73","order_by":2,"name":"Evren Değirmenci","email":"","orcid":"","institution":"Mersin University","correspondingAuthor":false,"prefix":"","firstName":"Evren","middleName":"","lastName":"Değirmenci","suffix":""},{"id":467873087,"identity":"5b93f87d-69db-4fc5-bdaf-3acd8a9aa237","order_by":3,"name":"İnci Kesilmiş","email":"","orcid":"","institution":"Mersin University","correspondingAuthor":false,"prefix":"","firstName":"İnci","middleName":"","lastName":"Kesilmiş","suffix":""},{"id":467873089,"identity":"b5adb756-6bc6-447e-b53c-b37eb28e25aa","order_by":4,"name":"Ülkü Çömelekoğlu","email":"","orcid":"","institution":"Mersin University","correspondingAuthor":false,"prefix":"","firstName":"Ülkü","middleName":"","lastName":"Çömelekoğlu","suffix":""}],"badges":[],"createdAt":"2025-05-03 23:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6586053/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6586053/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84182859,"identity":"0307725c-dcb9-4049-a070-57798eb753ef","added_by":"auto","created_at":"2025-06-09 04:31:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1637462,"visible":true,"origin":"","legend":"","description":"","filename":"EOGArticlewithauthordetailsrevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6586053/v1_covered_be9e5082-2273-465e-8016-2fdea7781b3a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Automated Detection of Quiet Eye Durations in Archery Using Electrooculography and Comparative Deep Learning Models","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":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Quiet eye, Electrooculography, Wavelet Transform, Convolutional Neural Networks, Long-Short Term Memory, Transformer, UNet, GRU","lastPublishedDoi":"10.21203/rs.3.rs-6586053/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6586053/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a deep learning-based approach for the automated detection of Quiet Eye (QE) durations from electrooculography (EOG) signals in archery. QE\u0026mdash;the final fixation or tracking of the gaze before executing a motor action\u0026mdash;is a critical factor in precision sports. Traditional detection methods, which rely on expert evaluations, are inherently subjective, time-consuming, and inconsistent. To overcome these limitations, EOG data were collected from 10 licensed archers during controlled shooting sessions and preprocessed using a wavelet transform and a Butterworth bandpass filter for noise reduction.\u003c/p\u003e \u003cp\u003eWe implemented and compared five deep learning models\u0026mdash;CNN\u0026thinsp;+\u0026thinsp;LSTM, CNN\u0026thinsp;+\u0026thinsp;GRU, Transformer, UNet, and 1D CNN\u0026mdash;for QE detection. The CNN\u0026thinsp;+\u0026thinsp;LSTM model achieved the highest accuracy (95%), followed closely by CNN\u0026thinsp;+\u0026thinsp;GRU (93%), demonstrating superior performance in capturing both spatial and temporal dependencies in the EOG signals. Although Transformer-based and UNet models performed competitively, they exhibited lower precision in distinguishing QE periods.\u003c/p\u003e \u003cp\u003eThese results indicate that deep learning provides an effective and scalable solution for objective QE analysis, substantially reducing the dependence on expert annotations. This automated approach can enhance sports training by offering real-time, data-driven feedback to athletes and coaches. Furthermore, the methodology holds promise for broader applications in cognitive and motor skill assessments across various domains. Future work will focus on expanding the dataset, enabling real-time deployment, and evaluating model generalizability across different skill levels and sports disciplines.\u003c/p\u003e","manuscriptTitle":"Automated Detection of Quiet Eye Durations in Archery Using Electrooculography and Comparative Deep Learning Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 04:07:52","doi":"10.21203/rs.3.rs-6586053/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-11T10:46:57+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"170150907774344711507921809534458169595","date":"2025-06-11T06:58:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-10T07:39:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-09T12:08:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49617691720434084324195976456678683582","date":"2025-06-09T08:00:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158152045846538835035466189246051552787","date":"2025-06-07T09:24:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103154977877181928878009123035732708490","date":"2025-06-06T16:13:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253301643076623557530803847351187763459","date":"2025-06-05T12:47:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-05T11:50:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-04T10:21:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-12T08:53:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-10T19:45:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2025-05-10T19:44:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f47df9aa-3ff6-45d4-83c8-c5bf62e2392b","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-25T06:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 04:07:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6586053","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6586053","identity":"rs-6586053","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}