Pipeline for Assisting Diagnosis of Children with Autism Spectrum Disorder via Automated Method for Classifying Repetitive Behaviors

dc.contributor.advisor Oh, HyunJoo
dc.contributor.author Sun, Jimin
dc.contributor.committeeMember Kim, Jennifer
dc.contributor.department Computer Science
dc.date.accessioned 2022-05-27T14:38:09Z
dc.date.available 2022-05-27T14:38:09Z
dc.date.created 2022-05
dc.date.issued 2022-05
dc.date.submitted May 2022
dc.date.updated 2022-05-27T14:38:09Z
dc.description.abstract We present automated methods to collect and provide motion data via sensors that can be used to assist the clinical decision-making process when diagnosing autism spectrum disorder (ASD) in children. The current limitation of assessment tools for diagnosing ASD in children is the subjectivity of the observer. We present a pipeline for automating the process of data collection when children interact with toys, specifically focusing on the repetitive behavior commonly portrayed among children with ASD. We used data that mimicked repetitive behaviors identified in different studies. Existing research and assessment of the diagnosis of ASD in children indicates the need for having an automated and quantifiable approach that can provide more than mere observation. It is important to note, however, that this can only aid existing methods of diagnosing autism, and its clinical relevance is to be further evaluated. It is critical for ASD to be detected at the early stages of the life of children. It has been supported that intervention for children with ASD is more effective in the younger age group and is beneficial for the long-term prognosis of children. Therefore, automated technology will potentially help increase the efficiency and objectivity of observation-based diagnostic procedures. We discuss how using simple technologies such as an Arduino board, could bring efficiency and objective analysis to the observation procedure in the diagnosis of ASD.
dc.description.degree Undergraduate
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/66735
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Autism
dc.subject ASD
dc.subject Machine learning
dc.subject HCI
dc.title Pipeline for Assisting Diagnosis of Children with Autism Spectrum Disorder via Automated Method for Classifying Repetitive Behaviors
dc.type Text
dc.type.genre Undergraduate Thesis
dspace.entity.type Publication
local.contributor.advisor Oh, HyunJoo
local.contributor.corporatename College of Computing
local.contributor.corporatename School of Computer Science
local.contributor.corporatename Undergraduate Research Opportunities Program
local.relation.ispartofseries Undergraduate Research Option Theses
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relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
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relation.isSeriesOfPublication e1a827bd-cf25-4b83-ba24-70848b7036ac
thesis.degree.level Undergraduate
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