Identifying activity level related movement features of children with ASD based on ADOS videos.

ABSTRACT: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects about 2% of children. Due to the shortage of clinicians, there is an urgent demand for a convenient and effective tool based on regular videos to assess the symptom. Computer-aided technologies have become widely used in clinical diagnosis, simplifying the diagnosis process while saving time and standardizing the procedure. In this study, we proposed a computer vision-based motion trajectory detection approach assisted with machine learning techniques, facilitating an objective and effective way to extract participants' movement features (MFs) to identify and evaluate children's activity levels that correspond to clinicians' professional ratings. The designed technique includes two key parts: (1) Extracting MFs of

SUBMITTER: Jin X 

PROVIDER: S-EPMC9975881 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature