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Automated Detection of Repetitive Motor Behaviors as an Outcome Measurement in Intellectual and Developmental Disabilities / K. H. GILCHRIST in Journal of Autism and Developmental Disorders, 48-5 (May 2018)
[article]
Titre : Automated Detection of Repetitive Motor Behaviors as an Outcome Measurement in Intellectual and Developmental Disabilities Type de document : Texte imprimé et/ou numérique Auteurs : K. H. GILCHRIST, Auteur ; M. HEGARTY-CRAVER, Auteur ; R. B. CHRISTIAN, Auteur ; S. GREGO, Auteur ; A. C. KIES, Auteur ; Anne C. WHEELER, Auteur Article en page(s) : p.1458-1466 Langues : Anglais (eng) Mots-clés : Accelerometer Activity recognition Motor stereotypy Neurodevelopmental disorders Repetitive behaviors Wearable sensor Index. décimale : PER Périodiques Résumé : Repetitive sensory motor behaviors are a direct target for clinical treatment and a potential treatment endpoint for individuals with intellectual or developmental disabilities. By removing the burden associated with video annotation or direct observation, automated detection of stereotypy would allow for longer term monitoring in ecologic settings. We report automated detection of common stereotypical motor movements using commercially available accelerometers affixed to the body and a generalizable detection algorithm. The method achieved a sensitivity of 80% for body rocking and 93% for hand flapping without individualized algorithm training or foreknowledge of subject's specific movements. This approach is well-suited for implementation in a continuous monitoring system outside of a clinical setting. En ligne : http://dx.doi.org/10.1007/s10803-017-3408-6 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=355
in Journal of Autism and Developmental Disorders > 48-5 (May 2018) . - p.1458-1466[article] Automated Detection of Repetitive Motor Behaviors as an Outcome Measurement in Intellectual and Developmental Disabilities [Texte imprimé et/ou numérique] / K. H. GILCHRIST, Auteur ; M. HEGARTY-CRAVER, Auteur ; R. B. CHRISTIAN, Auteur ; S. GREGO, Auteur ; A. C. KIES, Auteur ; Anne C. WHEELER, Auteur . - p.1458-1466.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 48-5 (May 2018) . - p.1458-1466
Mots-clés : Accelerometer Activity recognition Motor stereotypy Neurodevelopmental disorders Repetitive behaviors Wearable sensor Index. décimale : PER Périodiques Résumé : Repetitive sensory motor behaviors are a direct target for clinical treatment and a potential treatment endpoint for individuals with intellectual or developmental disabilities. By removing the burden associated with video annotation or direct observation, automated detection of stereotypy would allow for longer term monitoring in ecologic settings. We report automated detection of common stereotypical motor movements using commercially available accelerometers affixed to the body and a generalizable detection algorithm. The method achieved a sensitivity of 80% for body rocking and 93% for hand flapping without individualized algorithm training or foreknowledge of subject's specific movements. This approach is well-suited for implementation in a continuous monitoring system outside of a clinical setting. En ligne : http://dx.doi.org/10.1007/s10803-017-3408-6 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=355 Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques / Kristine D. CANTIN-GARSIDE in Journal of Autism and Developmental Disorders, 50-11 (November 2020)
[article]
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
in Journal of Autism and Developmental Disorders > 50-11 (November 2020) . - p.4039-4052[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