# Multimodal physiological monitoring in augmented reality teaching environments for children with neurodevelopmental disorders

## TL;DR

This study shows how combining AR teaching with physiological monitoring can improve learning for children with neurodevelopmental disorders.

## Contribution

A novel multimodal fusion approach for identifying disorder-specific patterns in AR-based education.

## Key findings

- Multimodal fusion achieved 89.3% accuracy in identifying disorder-specific physiological patterns.
- AR environments reduced cognitive load by 27% while maintaining engagement.
- Personalized interventions improved attention and social interaction scores by 31.2% and 24.8%, respectively.

## 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.