Behavioral Classification and Characterization of Autism Spectrum Disorder in Naturalistic Settings using Classical Machine Learning

Abstract

Autism Spectrum Disorder (ASD) is a group of complex neurodevelopmental disorders that affects about 1% of the world’s population, impacting the quality of life of not only the diagnosed individuals but also their communities. Early detection and intervention are paramount to limit its effect on a child’s development, however overlap with other disorders and medical comorbidities make these tasks challenging. The present study explores the use of a novel multimodal, interpretable approach to characterize ASD children’s behavior in a naturalistic environment. Spatial (real-time location tracking), audio and demographic data from children in a classroom setting are integrated and analyzed to identify traits potentially connected to ASD. Our findings point to the use of this type of approach as a potential tool for screening individuals in a naturalistic setting, allowing for further evaluation and, ultimately, earlier diagnosis by a clinician. Results show good classification performance and suggest vocalization, speech, proximity and certain movement-related features to be impacted in ASD.