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

Alessandro Crippa, Christian Salvatore, Paolo Perego, Sara Forti, Maria Nobile, Massimo Molteni, Isabella Castiglioni

Research output: Contribution to journalArticle

35 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2146-2156
Number of pages11
JournalJournal of Autism and Developmental Disorders
Volume45
Issue number7
DOIs
Publication statusPublished - Jul 19 2015

Fingerprint

Autistic Disorder
Preschool Children
Biomechanical Phenomena
Upper Extremity
Phenotype
Autism Spectrum Disorder
Machine Learning

Keywords

  • Autism spectrum disorder
  • Classification
  • Kinematics
  • Machine learning
  • Support vector machines

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Medicine(all)

Cite this

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.

In: Journal of Autism and Developmental Disorders, Vol. 45, No. 7, 19.07.2015, p. 2146-2156.

Research output: Contribution to journalArticle

Crippa, Alessandro ; Salvatore, Christian ; Perego, Paolo ; Forti, Sara ; Nobile, Maria ; Molteni, Massimo ; Castiglioni, Isabella. / Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities. In: Journal of Autism and Developmental Disorders. 2015 ; Vol. 45, No. 7. pp. 2146-2156.
@article{4cf908b9c68f4d1fa5209d22bc3b5f5c,
title = "Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities",
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.",
keywords = "Autism spectrum disorder, Classification, Kinematics, Machine learning, Support vector machines",
author = "Alessandro Crippa and Christian Salvatore and Paolo Perego and Sara Forti and Maria Nobile and Massimo Molteni and Isabella Castiglioni",
year = "2015",
month = "7",
day = "19",
doi = "10.1007/s10803-015-2379-8",
language = "English",
volume = "45",
pages = "2146--2156",
journal = "Journal of Autism and Developmental Disorders",
issn = "0162-3257",
publisher = "Springer New York",
number = "7",

}

TY - JOUR

T1 - Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities

AU - Crippa, Alessandro

AU - Salvatore, Christian

AU - Perego, Paolo

AU - Forti, Sara

AU - Nobile, Maria

AU - Molteni, Massimo

AU - Castiglioni, Isabella

PY - 2015/7/19

Y1 - 2015/7/19

N2 - 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.

AB - 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.

KW - Autism spectrum disorder

KW - Classification

KW - Kinematics

KW - Machine learning

KW - Support vector machines

UR - http://www.scopus.com/inward/record.url?scp=84931576479&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84931576479&partnerID=8YFLogxK

U2 - 10.1007/s10803-015-2379-8

DO - 10.1007/s10803-015-2379-8

M3 - Article

C2 - 25652603

AN - SCOPUS:84931576479

VL - 45

SP - 2146

EP - 2156

JO - Journal of Autism and Developmental Disorders

JF - Journal of Autism and Developmental Disorders

SN - 0162-3257

IS - 7

ER -