Multimodal physiological monitoring in augmented reality teaching environments for children with neurodevelopmental disorders
Year: 2026
Type: article
Abstract: This study investigates the integration of augmented reality (AR) teaching environments with multimodal physiological monitoring for children with neurodevelopmental disorders. We collected EEG, ECG, and eye-tracking data from 115 children (ASD n = 45, ADHD n = 38, SLD n = 32) during AR-enhanced learning tasks. The multimodal fusion approach achieved 89.3% classification accuracy in identifying disorder-specific patterns. Key biomarkers included frontal theta power variations ( p < 0.001), heart rate variability indices (LF/HF ratio), and fixation duration patterns. AR environments reduced cognitive load by 27% compared to traditional settings while maintaining engagement levels. Personalized intervention based on real-time physiological feedback improved attention performance by 31.2% and social interaction scores by 24.8% over 12 months. These findings demonstrate the efficacy of combining AR technology with physiological monitoring for adaptive special education.
Source: Frontiers in Human Neuroscience
Institution: Sehan University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7122588844