Predicting ADHD Risk from Touch Interaction Data

Abstract

This paper presents a novel approach for automatic prediction of risk of ADHD in schoolchildren based on touch interaction data. We performed a study with 129 fourth-grade students solving math problems on a multiple-choice interface to obtain a large dataset of touch trajectories. Using Support Vector Machines, we analyzed the predictive power of such data for ADHD scales. For regression of overall ADHD scores, we achieve a mean squared error of 0.0962 on a four-point scale (R² = 0.5667). Classification accuracy for increased ADHD risk (upper vs. lower third of collected scores) is 91.1%.