Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques.
J Autism Dev Disord
; 50(11): 4039-4052, 2020 Nov.
Article
em En
| MEDLINE
| ID: mdl-32219634
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Comportamento Autodestrutivo
/
Transtorno do Espectro Autista
/
Aprendizado de Máquina
Limite:
Adolescent
/
Child
/
Child, preschool
/
Female
/
Humans
/
Male
Idioma:
En
Revista:
J Autism Dev Disord
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos