ABSTRACT
Autism spectrum disorder (ASD) impacts communication and cognitive
development of children and adults, has aworldwide prevalence
of 1% on children and affects not only the people with this disorder,
but also their family and the surrounding community. In the family
circle, individuals on the spectrum require greater support and attention
relative to its cognitive capacity, impacting the mental and
emotional health and even the financial life of families. The lack of
infrastructure, professionals, and public health policies to deal with
ASD is a known problem, specially in low income countries. To
mitigate this issue, computer-aided ASD diagnosis and treatment
represent a powerful ally, reducing the workload of professionals
and allowing a better overall therapeutic experience. This paper
intends to investigate how machine learning techniques can help
specialists by providing an automated analysis of ASD recorded
therapy sessions. The proposed solution is capable of handling large
amounts of video data, filtering out irrelevant frames and keeping
only relevant scenes for posterior analysis. Our results show that
the proposed solution is capable of reducing manual checks by up
to 51.4%, which represents a significant workload reduction for
health experts. This solution will hopefully provide researchers,
therapists and specialists with a tool that assists the automated
identification of features and events of interest in video-recorded
therapy sessions, reducing the amount of time spent on this task.