Ontology-based Activity Intention Recognition via Human-in-the-loop Framework

Ontology-based Activity Intention Recognition via Human-in-the-loop Framework | 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 Ontology-based Activity Intention Recognition via Human-in-the-loop Framework Taejung Heo, Seokju Hwang, Yeseul Gong, Donghyun Kim, Kyong-Ho Lee, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4417393/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 Human intention recognition is crucial for various human-centered settings.Existing approaches can be categorized into data-driven and knowledge-drivenmethods. While data-driven methods have shown promising performance withlarge training sets, they face challenges in data collection and limited interpretability.In contrast, knowledge-driven methods have the advantage of notrequiring training data and being interpretable, but acquiring high-qualitydomain knowledge is time-consuming and costly. To address these challenges,we propose OntoIR, a novel Ontology-based human activity Intention Recognitionsystem with a human-in-the-loop rule generation and refinement framework.OntoIR involves an ontology and human-guided logical rules to recognize humanactivities and intentions. By Leveraging rule mining model, the OntoIR extractscandidate rules enriches them with contextual information. Human experts thencurate meaningful rules and supplement insufficient context information. Thisallows us to obtain explainable human intention recognition results throughexpressive rules in situations where data is scarce. To evaluate our system,we collect an Activity/Intention recognition dataset, AICar, in a car showroomenvironment. Comprehensive experiments are conducted on the AICar,demonstrating that the collaboration of rule-mining models and domain expertsimproves the performance of activity intention recognition without lowering theinterpretability of recognition results. Human intention recognition Human-in-the-loop system Rule-based reasoning 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. 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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-4417393","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305362191,"identity":"52f1b708-72f7-4b60-bc9e-d85c9694287b","order_by":0,"name":"Taejung Heo","email":"","orcid":"","institution":"Yonsei University","correspondingAuthor":false,"prefix":"","firstName":"Taejung","middleName":"","lastName":"Heo","suffix":""},{"id":305362195,"identity":"54270fad-ca7b-42a3-9d2c-3516c5f3f913","order_by":1,"name":"Seokju Hwang","email":"","orcid":"","institution":"Yonsei 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