Optimizing Fixation and Saccade Classification for ADHD Assessment Using NiCATS and IVA-2

This study explores enhanced methods for accurately identifying Attention Deficit Hyperactivity Disorder (ADHD) indicators in college students. ADHD, a neurodevelopmental disorder, impacts attention, impulse control, and emotional regulation, often leading to academic and social difficulties. Many students remain undiagnosed due to symptom overlap with stress and other factors. Traditional tools like the IVA-2 assess behavioral responses but may not fully capture ADHD complexity. This research integrates IVA-2 data with the eye-tracking metrics from the Non-Intrusive Classroom Attention Tracking System (NiCATS). It then compares two widely used gaze classification algorithms-Dispersion-Threshold Identification (IDT) and Velocity-Threshold Identification (IVT) to evaluate their effectiveness in distinguishing fixations and saccades. The results demonstrate that IVT with an optimized $275 \text{px} / \text{sec}$ threshold provides a more accurate representation of gaze patterns, reducing fixation over-segmentation and improving saccade detection. These findings support the use of optimized velocity thresholds for ADHD assessment and highlight the potential of data-driven approaches for enhancing diagnostic insights and personalized interventions.