Neuroassist: Advance ADHD Supporting System

ADHD (attention deficit hyperactivity disorder) is a neurodevelopmental condition that impacts children globally, leading to challenges with inattention, hyperactivity, and impulsiveness. This research proposes an AI-powered cognitive training system that utilizes machine learning (ML) and Continuous Performance Tests (CPT) to bridge the cognitive skill gap between ADHD and non-ADHD peers. A computerized Go/No-Go task is administered to non-ADHD children to establish baseline performance metrics. The same task is given to ADHD children, and the collected data is used to train a Random Forest model to identify cognitive deficits based on baseline comparisons. Based on these classifications, the system recommends tailored cognitive training activities, integrating gamification to enhance engagement and learning outcomes. The proposed approach ensures data-driven intervention strategies, addressing the limitations of conventional ADHD management. By continuously adapting to children’s progress, the system bridges the cognitive skill gap between ADHD and non-ADHD peers, fostering improved attention and impulse control. This research highlights the potential of AI-driven personalized interventions in transforming ADHD management, offering a scalable and effective solution for cognitive skill development.