Development of a Multiple RGB-D Sensor System for ADHD Screening and Improvement of Classification Performance Using Feature Selection Method

Abstract

Attention deficit and hyperactivity disorder (ADHD) is a mixed behavioral disorder that exhibits symptoms, such as carelessness and hyperactivity–impulsivity. To date, existing ADHD diagnosis methods rely on observations by observers, such as parents and teachers, which limits the ability to reflect objective evaluation. In this study, to overcome this limitation, we proposed a multiple RGB-D sensor system that can objectively measure the amount of action and attention of children playing a robot-led game. In addition, a classifier was developed to classify children into ADHD, ADHD risk, and normal groups using the multilayer perceptron and data obtained through sensors. The effectiveness of the developed system for ADHD screening was verified. In this study, the priority of abnormal behavior indicators designed for ADHD screening was measured, the features with the highest priority were selected using a feature selection method. Eight hundred and twenty-eight children participated and were classified into the ADHD, ADHD risk, and normal groups, and the results were compared with the diagnosis by clinicians. The proposed system achieved sensitivity of 97.06% and 100%, and specificity of 96.42% and 94.68% in the ADHD and ADHD risk groups, respectively.

Keywords

• ADHD screening
• robot-led ADHD screening game
• collective intelligence
• deep learning
• sensing system