Introducing SSBD+ Dataset with a Convolutional Pipeline for detecting Self-Stimulatory Behaviours in Children using raw videos
This work addresses the need for objective, automated tools to assist in diagnosing autism spectrum disorder in children, though it appears incremental as it builds on an existing dataset and method.
The authors tackled the problem of automating the detection of self-stimulatory behaviors for autism diagnosis by proposing a pipelined deep learning architecture and an augmented dataset, achieving an overall accuracy of around 81%.
Conventionally, evaluation for the diagnosis of Autism spectrum disorder is done by a trained specialist through questionnaire-based formal assessments and by observation of behavioral cues under various settings to capture the early warning signs of autism. These evaluation techniques are highly subjective and their accuracy relies on the experience of the specialist. In this regard, machine learning-based methods for automated capturing of early signs of autism from the recorded videos of the children is a promising alternative. In this paper, the authors propose a novel pipelined deep learning architecture to detect certain self-stimulatory behaviors that help in the diagnosis of autism spectrum disorder (ASD). The authors also supplement their tool with an augmented version of the Self Stimulatory Behavior Dataset (SSBD) and also propose a new label in SSBD Action detection: no-class. The deep learning model with the new dataset is made freely available for easy adoption to the researchers and developers community. An overall accuracy of around 81% was achieved from the proposed pipeline model that is targeted for real-time and hands-free automated diagnosis. All of the source code, data, licenses of use, and other relevant material is made freely available in https://github.com/sarl-iiitb/