AI- BASED ADHD DETECTION IN CHILDREN USING BODY POSE ANALYSIS

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition that significantly impairs classroom performance and academic achievement. Traditional screening relies on subjective clinical assessments, which are time-consuming and prone to observer bias. This paper presents an automated ADHD behavioral screening system that leverages computer vision and machine learning to analyze observable behavioral indicators from classroom video recordings. Eight behavioral features—yaw standard deviation, head turn ratio, average gaze deviation, sustained focus ratio, body motion energy, hand fidget frequency, mean wrist velocity, and posture ratio—are extracted per frame using MediaPipe Face Mesh and Pose estimation. A Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel and StandardScaler normalization classifies behavioral patterns as ADHD-indicative or typical. Five-fold cross-validation yields a mean accuracy of 91.4%, with an Area Under the ROC Curve (AUC) of 0.963, demonstrating the efficacy of the proposed noninvasive screening pipeline.