[article] 
inJournal of Autism and Developmental Disorders > 50-11 (November 2020) . - p.4039-4052

Titre :	Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques
Type de document :	Texte imprimé et/ou numérique
Auteurs :	Kristine D. CANTIN-GARSIDE, Auteur ; Zhenyu KONG, Auteur ; Susan W. WHITE, Auteur ; Ligia ANTEZANA, Auteur ; Sunwook KIM, Auteur ; Maury A. NUSSBAUM, Auteur
Article en page(s) :	p.4039-4052
Langues :	Anglais (eng)
Mots-clés :	Activity recognition  Autism  Machine learning  Wearable sensors
Index. décimale :	PER Périodiques
Résumé :	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.
En ligne :	http://dx.doi.org/10.1007/s10803-020-04463-x
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=432

[article] Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques [Texte imprimé et/ou numérique] / Kristine D. CANTIN-GARSIDE, Auteur ; Zhenyu KONG, Auteur ; Susan W. WHITE, Auteur ; Ligia ANTEZANA, Auteur ; Sunwook KIM, Auteur ; Maury A. NUSSBAUM, Auteur . - p.4039-4052.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 50-11 (November 2020) . - p.4039-4052

Mots-clés :	Activity recognition  Autism  Machine learning  Wearable sensors
Index. décimale :	PER Périodiques
Résumé :	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.
En ligne :	http://dx.doi.org/10.1007/s10803-020-04463-x
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=432