Context-based Dataset for Analysis of Videos of Autistic Children

Autism affects as many as 1 in 44 youth, with many higher-functioning children not diagnosed until school-age or later. Currently, diagnosing autism is a lengthy process often delivered in varying settings (i.e., context) by a multi-disciplinary team, where the result can include subjective bias. Automatic approaches that can help professionals with diagnosis can result in earlier and quicker diagnosis. To help facilitate development of automatic approaches, we present a new, context-based dataset for analysis of videos of autistic children. The data was collected from 14 children, using the gold-standard RITA-T evaluation. Along with the dataset we also provide a baseline, context-based approach for classification of these videos. The baseline shows encouraging results that context matters for classification, and we also detail findings about which features (face, body, or gaze) are most encouraging within those contexts.