Managing Classroom Behavior in School-Aged Children With ADHD Using AI-Empowered Vest

Area of Science:

• Neuroscience
• Artificial Intelligence
• Human-Computer Interaction

Background:

• Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in school-aged children.
• Current classroom management strategies for ADHD lack immediate, automated interventions.
• There is a need for innovative technological solutions to support children with ADHD in educational settings.

Purpose of the Study:

• To develop and evaluate an AI-powered vest for real-time monitoring and intervention of hyperactive behavior in children with ADHD.
• To assess the accuracy of the AI system in classifying typical versus hyperactive behavior.
• To determine the effectiveness of vibration-based interventions in reducing hyperactivity and improving classroom performance.

Main Methods:

• An AI-powered vest equipped with inertial measurement units was developed to collect behavioral data.
• A neural network was trained to classify child behavior as typical or hyperactive.
• A controlled experiment with 40 children (ages 7-12) compared behavior and performance with and without vibration interventions.
• Teacher ratings on a five-point scale were used to assess performance.

Main Results:

• The AI vest achieved a classification and intervention accuracy of 0.84 on the test set.
• A significant reduction in children's activity levels was observed following vibration interventions.
• Teacher-rated performance significantly improved in classroom sections where the vibration intervention was applied (p < 0.05).

Conclusions:

• The AI-powered vest offers a promising real-time solution for managing hyperactive behavior in children with ADHD in classroom settings.
• The system demonstrates potential for improving both behavioral regulation and academic performance through automated, non-intrusive interventions.