• Resumo

    Event detection in therapy sessions for children with Autism

    Data de publicação: 03/05/2023

    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.

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