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Journal of Autism and Developmental Disorders

Gepubliceerd in:

26-03-2020 | Original Paper

Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques

Auteurs: Kristine D. Cantin-Garside, Zhenyu Kong, Susan W. White, Ligia Antezana, Sunwook Kim, Maury A. Nussbaum

Gepubliceerd in: Journal of Autism and Developmental Disorders | Uitgave 11/2020

Abstract

Traditional self-injurious behavior (SIB) management can place compliance demands on the caregiver and have low ecological validity and accuracy. To support an SIB monitoring system for autism spectrum disorder (ASD), we evaluated machine learning methods for detecting and distinguishing diverse SIB types. SIB episodes were captured with body-worn accelerometers from children with ASD and SIB. The highest detection accuracy was found with k-nearest neighbors and support vector machines (up to 99.1% for individuals and 94.6% for grouped participants), and classification efficiency was quite high (offline processing at ~ 0.1 ms/observation). Our results provide an initial step toward creating a continuous and objective smart SIB monitoring system, which could in turn facilitate the future care of a pervasive concern in ASD.

vorige artikel Executive Function in High-Functioning Autism Spectrum Disorder: A Meta-analysis of fMRI Studies

volgende artikel Resilience in Familial Caregivers of Children with Developmental Disabilities: A Meta-analysis

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Titel: Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques
Auteurs: Kristine D. Cantin-Garside
Zhenyu Kong
Susan W. White
Ligia Antezana
Sunwook Kim
Maury A. Nussbaum
Publicatiedatum: 26-03-2020
Uitgeverij: Springer US
Gepubliceerd in: Journal of Autism and Developmental Disorders / Uitgave 11/2020
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI: https://doi.org/10.1007/s10803-020-04463-x

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