Establishing Objective Ground-Truth for PediatricADHD Engagement: A Methodological Frameworkand Benchmark Dataset | 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 Establishing Objective Ground-Truth for PediatricADHD Engagement: A Methodological Frameworkand Benchmark Dataset Somayeh Malekshahi, Reza Rostami, Hamid Soltanian-Zadeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8497702/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Detecting engagement in pediatric ADHD typically relies on subjective, biasedhuman labeling. To address this, we introduce an objective annotation framework utilizing behavioral performance from the Integrated Visual and AuditoryContinuous Performance Test (IVA-2). From 21 children, we derived a continuousEngagement Score (Es) and windowed categorical labels, validating them againstofficial IVA-2 clinical scales. The proposed score demonstrated excellent internalreliability (r = 0.84), strong convergent validity (AUC = 0.97), and clinical sensitivity, significantly differentiating ADHD severity groups (Kruskal–Wallis, p <0.05). Temporal analysis revealed a strong concurrent correlation (r = 0.57) withreal-time dynamics. Benchmarking established that static Random Forest modelscurrently outperform temporal baselines, with eye-gaze dynamics serving as thestrongest unimodal predictor. We release the synchronized video features (OpenFace 2.0) and validated labels, providing a robust, privacy-preserving benchmarkfor objective engagement detection in clinical and educational settings. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Biological sciences/Psychology Social science/Psychology Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryDataS1Code.zip SupplementaryDataS2Dataset.zip Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 May, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers invited by journal 24 Mar, 2026 Editor assigned by journal 24 Mar, 2026 Editor invited by journal 09 Jan, 2026 Submission checks completed at journal 08 Jan, 2026 First submitted to journal 08 Jan, 2026 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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