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 participants' different body key points in various activities segmented from autism diagnostic observation schedule (ADOS) videos, and (2) Identifying the most relevant MFs through established correlations with existing data sets of participants' activity level scores evaluated by clinicians. The research investigated two types of MFs, i.e., pixel distance (PD) and instantaneous pixel velocity (IPV), three participants' body key points, i.e., neck, right wrist, and middle hip, and five activities, including Table-play, Birthday-party, Joint-attention, Balloon-play, and Bubble-play segmented from ADOS videos. Among different combinations, the high correlations with the activity level scores evaluated by the clinicians (greater than 0.6 with p < 0.001) were found in Table-play activity for both the PD-based MFs of all three studied key points and the IPV-based MFs of the right wrist key point. These MFs were identified as the most relevant ones that could be utilized as an auxiliary means for automating the evaluation of activity levels in the ASD assessment.