ArticlePloS one2017
Applying machine learning to identify autistic adults using imitation: An exploratory study.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- SHAP-based explainable AI framework for autism severity classification using 3D motor biomarkers.Frontiers in psychiatry · 2026Article
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods.Journal of autism and developmental disorders · 2025Review
- Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks.Healthcare technology letters · 2024Article
- Can People With Higher Versus Lower Scores on Impression Management or Self-Monitoring Be Identified Through Different Traces Under Faking?Educational and psychological measurement · 2024Article
- Decreased wrist rotation imitation abilities in children with autism spectrum disorder.Frontiers in psychiatry · 2024Article
- [Differential diagnosis of autism spectrum disorder and global developmental delay based on machine learning and Children Neuropsychological and Behavioral Scale].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2023Article
- A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism.Annual review of biomedical data science · 2023Review
- Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach.Journal of autism and developmental disorders · 2023Article
- Machine Learning-Based Blood RNA Signature for Diagnosis of Autism Spectrum Disorder.International journal of molecular sciences · 2023Article
- Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms.Journal of autism and developmental disorders · 2022Article
- Identifying neuroanatomical and behavioral features for autism spectrum disorder diagnosis in children using machine learning.PloS one · 2022Article
- Computational Measurement of Motor Imitation and Imitative Learning Differences in Autism Spectrum Disorder: Computational Motor Imitation Measurement in ASD.ICMI '21 companion : companion publication of the 2021 International Conference on Multimodal Interaction : October 18th-22, 2021, Montreal, Canada. ICMI (Conference) (23rd : 2021 : Montreal, Quebec; Online) · 2021Article
- Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation.Journal of medical Internet research · 2021Article
- Computerized Assessment of Motor Imitation as a Scalable Method for Distinguishing Children With Autism.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2021Article
- Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia.Scientific reports · 2021Article
- The mouse model of fragile X syndrome exhibits deficits in contagious itch behavior.Scientific reports · 2020Article
- Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements.Seminars in pediatric neurology · 2020Review
- Applying Machine Learning to Kinematic and Eye Movement Features of a Movement Imitation Task to Predict Autism Diagnosis.Scientific reports · 2020Article
- Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality.Frontiers in human neuroscience · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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