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.

Crippa, A., Salvatore, C., Perego, P., Forti, S., Nobile, M., Molteni, M., et al. (2015). Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities. JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS, 45(7), 2146-2156 [10.1007/s10803-015-2379-8].

Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities

CRIPPA, ALESSANDRO
;
SALVATORE, CHRISTIAN;CASTIGLIONI, ISABELLA
2015

Abstract

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.
Articolo in rivista - Articolo scientifico
Autism spectrum disorder; Classification; Kinematics; Machine learning; Support vector machines;
Autism spectrum disorder; Classification; Kinematics; Machine learning; Support vector machines; Developmental and Educational Psychology
English
2015
45
7
2146
2156
reserved
Crippa, A., Salvatore, C., Perego, P., Forti, S., Nobile, M., Molteni, M., et al. (2015). Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities. JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS, 45(7), 2146-2156 [10.1007/s10803-015-2379-8].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/88508
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