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ERIC Number: EJ1065200
Record Type: Journal
Publication Date: 2015-Jul
Pages: 11
Abstractor: As Provided
Reference Count: 48
ISSN: ISSN-0162-3257
Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities
Crippa, Alessandro; Salvatore, Christian; Perego, Paolo; Forti, Sara; Nobile, Maria; Molteni, Massimo; Castiglioni, Isabella
Journal of Autism and Developmental Disorders, v45 n7 p2146-2156 Jul 2015
In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2-4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children with ASD from 15 typically developing children by means of kinematic analysis of a simple reach-to-drop task. Our method reached a maximum classification accuracy of 96.7 % with seven features related to the goal-oriented part of the movement. These preliminary findings offer insight into a possible motor signature of ASD that may be potentially useful in identifying a well-defined subset of patients, reducing the clinical heterogeneity within the broad behavioral phenotype.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A