2026-10-11T14:50:49Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33922147 2026-09-18T07:10:56Z category_7 portal_147 item_type_3 month_year_09_2026

Supplemental Material for:A Single-Center Retrospective Analysis of Artificial Intelligence-Based Emotion Recognition Systems for Attention-Regulation Training in Children with Attention-Deficit/Hyperactivity Disorder figshare admin karger (2628495) Chenggang Chen (23511309) Chunbao Zhi (25070762) Lili Zhu (219363) BinRong Chen (20675129) Tingting Wang (123983) Medicine medicine <p dir="ltr"><b>Introduction:</b> Children with attention-deficit/hyperactivity disorder (ADHD) often experience attention instability and emotion dysregulation. Existing interventions typically lack real-time responsiveness and ecological validity. This study retrospectively examined historical training records from an AI-assisted closed-loop system integrating emotion recognition and behavioral feedback for attention regulation in children with ADHD.</p><p dir="ltr"><b>Methods:</b> This single-center retrospective analysis included historical training records from 60 children with ADHD who had completed either AI-assisted attention training or conventional attention training between 2021 and 2024. The AI-assisted training-record group comprised children whose archived records included 12 sessions of integrated cognitive training involving Go/No-Go, CPT, and Flanker tasks with real-time AI-generated feedback based on task performance and facial-emotion monitoring. The conventional training-record group completed the same attention tasks without AI-based feedback. Assessments included task performance, Conners Continuous Performance Test, 3rd Edition (CPT-3), teacher ratings, and emotion metrics, analyzed using linear mixed-effects models, linear growth modeling, and structural equation modeling (SEM).</p><p dir="ltr"><b>Results:</b> Compared with conventional training records, AI-assisted training records showed greater pre-post changes in reaction time (RT), RT variability, and task accuracy, together with fewer commission errors in CPT-3. Post-feedback behavioral recovery was observed in 78.3% of feedback events. Teacher ratings showed consistent pre-post improvements across attention maintenance, emotional regulation, and task execution. Negative emotion labels decreased from 42.3% to 24.6% (p = 0.003). SEM supported an associative pathway linking emotion recognition frequency, feedback frequency, and RT reduction (95% CI: 0.15-0.38).</p><p dir="ltr"><b>Conclusions:</b> The retrospective findings suggest that AI-assisted closed-loop training records were associated with favorable changes in attention and emotional-state indicators in children with ADHD. These exploratory results support further prospective, externally validated studies before broader clinical or educational implementation.</p> 2026-09-18T07:10:56Z Dataset Dataset 10.6084/m9.figshare.33922147.v1 https://figshare.com/articles/dataset/Supplemental_Material_for_A_Single-Center_Retrospective_Analysis_of_Artificial_Intelligence-Based_Emotion_Recognition_Systems_for_Attention-Regulation_Training_in_Children_with_Attention-Deficit_Hyperactivity_Disorder/33922147 CC BY 4.0