@article{Crippa2015,
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.},
author = {Crippa, Alessandro and Salvatore, Christian and Perego, Paolo and Forti, Sara and Nobile, Maria and Molteni, Massimo and Castiglioni, Isabella},
doi = {10.1007/s10803-015-2379-8},
journal = {Journal of Autism and Developmental Disorders},
number = {7},
title = {{Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities}},
volume = {45},
pages = {2146-2156},
year = {2015},
url = {https://link.springer.com/article/10.1007/s10803-015-2379-8},
note = {[Role: coauthor]}
}

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