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PLoS One. 2017 Aug 16;12(8):e0182652. doi: 10.1371/journal.pone.0182652. eCollection 2017.

Applying machine learning to identify autistic adults using imitation: An exploratory study.

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Department of Computer Science, Loughborough University, Loughborough, United Kingdom.
Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.


Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was based on a previous task where 16 ASC participants and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 kinematic parameters extracted from eight imitation conditions were analysed using machine learning based methods. Two optimal imitation conditions and nine most significant kinematic parameters were identified and compared with some standard attribute evaluators. To our knowledge, this is the first attempt to apply machine learning to kinematic movement parameters measured during imitation of hand movements to investigate the identification of ASC. Although based on a small sample, the work demonstrates the feasibility of applying machine learning methods to analyse high-dimensional data and suggest the potential of machine learning for identifying kinematic biomarkers that could contribute to the diagnostic classification of autism.

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