Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques.
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...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 50; no. 11; pp. 4039 - 4053 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146431887&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146431887 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: Nov2020 vid: 50 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146431887 144090233 146431887 146431887 10.1007/s10803-020-04463-x 146431887 ppf: 4039 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques. aug: au: Cantin-Garside, Kristine D. Kong, Zhenyu White, Susan W. Antezana, Ligia Kim, Sunwook Nussbaum, Maury A. affil: Department of Industrial and Systems Engineering, Virginia Tech, 24060, Blacksburg, VA, USA sug: subj: Self-Injurious Behavior Diagnosis Self-Injurious Behavior Classification Machine Learning Methods Machine Learning Utilization Autism Spectrum Disorder Human Female Male Child Accelerometers Validity Wearable Sensors Monitoring, Physiologic Descriptive Statistics Child: 6-12 years Female Male ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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