Statistical Analysis and Multimodal Classification on Noisy Eye Tracker and Application Log Data of children with Autism and ADHD

Abstract

Emotion recognition behavior and performance may vary between people with major neurodevelopmental disorders such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and control groups. It is crucial to identify these differences for early diagnosis and individual treatment purposes. This study represents a methodology by using statistical data analysis and machine learning to provide help to psychiatrists and therapists on the diagnosis and individualized treatment of participants with ASD and ADHD. In this paper we propose an emotion recognition experiment environment and collect eye tracker fixation data together with the application log data (APL). In order to detect the diagnosis of the participant we used classification algorithms with the Tomek links noise removing method. The highest classification accuracy results were reported as 86.36% for ASD vs. Control, 81.82% for ADHD vs. Control and 70.83% for ASD vs. ADHD. This study provides evidence that fixation and APL data have distinguishing features for the diagnosis of ASD and ADHD.