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

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Title: Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities
Language: English
Authors: Crippa, Alessandro, Salvatore, Christian, Perego, Paolo
Source: Journal of Autism and Developmental Disorders. Jul 2015 45(7):2146-2156.
Availability: Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
Peer Reviewed: Y
Page Count: 11
Publication Date: 2015
Document Type: Journal Articles
Reports - Research
Descriptors: Children, Autism, Psychomotor Skills, Task Analysis, Identification, Classification, Preschool Children, Patients, Genetics, Motor Reactions, Accuracy, Equipment, Evaluation Methods, Goal Orientation
DOI: 10.1007/s10803-015-2379-8
ISSN: 0162-3257
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.
Abstractor: As Provided
Number of References: 48
Entry Date: 2015
Accession Number: EJ1065200
Database: ERIC
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  Data: Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities
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  Data: <searchLink fieldCode="AR" term="%22Crippa%2C+Alessandro%22">Crippa, Alessandro</searchLink><br /><searchLink fieldCode="AR" term="%22Salvatore%2C+Christian%22">Salvatore, Christian</searchLink><br /><searchLink fieldCode="AR" term="%22Perego%2C+Paolo%22">Perego, Paolo</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Autism+and+Developmental+Disorders%22"><i>Journal of Autism and Developmental Disorders</i></searchLink>. Jul 2015 45(7):2146-2156.
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  Data: Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
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  Data: <searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Autism%22">Autism</searchLink><br /><searchLink fieldCode="DE" term="%22Psychomotor+Skills%22">Psychomotor Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Task+Analysis%22">Task Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Identification%22">Identification</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Preschool+Children%22">Preschool Children</searchLink><br /><searchLink fieldCode="DE" term="%22Patients%22">Patients</searchLink><br /><searchLink fieldCode="DE" term="%22Genetics%22">Genetics</searchLink><br /><searchLink fieldCode="DE" term="%22Motor+Reactions%22">Motor Reactions</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Equipment%22">Equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Goal+Orientation%22">Goal Orientation</searchLink>
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  Data: 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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  Data: 2015
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        Value: 10.1007/s10803-015-2379-8
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      – SubjectFull: Task Analysis
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      – SubjectFull: Identification
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      – SubjectFull: Classification
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      – SubjectFull: Preschool Children
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      – SubjectFull: Patients
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      – SubjectFull: Genetics
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      – SubjectFull: Motor Reactions
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      – SubjectFull: Accuracy
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      – SubjectFull: Equipment
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      – SubjectFull: Evaluation Methods
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      – SubjectFull: Goal Orientation
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    Titles:
      – TitleFull: Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities
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            NameFull: Perego, Paolo
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