ArticleScientific reports2020
Applying Machine Learning to Kinematic and Eye Movement Features of a Movement Imitation Task to Predict Autism Diagnosis.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 79 citations in OpenAlex.
- Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder.Algorithms · 2025Article
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data.Bioengineering (Basel, Switzerland) · 2025Review
- A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods.Journal of autism and developmental disorders · 2025Review
- Effective Autism Classification Through Grasping Kinematics.Autism research : official journal of the International Society for Autism Research · 2025Article
- Early detection of autism spectrum disorder: gait deviations and machine learning.Scientific reports · 2025Article
- Article
- Risk assessment and automatic identification of autistic children based on appearance.Scientific reports · 2024Article
- Commercial Wearables for the Management of People with Autism Spectrum Disorder: A Review.Biosensors · 2024Review
- Level of autistic traits in neurotypical adults predicts kinematic idiosyncrasies in their biological movements.Frontiers in integrative neuroscience · 2024Article
- A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism.Annual review of biomedical data science · 2023Review
- Motor differences in autism during a human-robot imitative gesturing task.Clinical biomechanics (Bristol, Avon) · 2023Article
- From "one big clumsy mess" to "a fundamental part of my character." Autistic adults' experiences of motor coordination.PloS one · 2023Article
- Digitally assisted diagnostics of autism spectrum disorder.Frontiers in psychiatry · 2023Review
- The whole-body motor skills of children with autism spectrum disorder taking goal-directed actions in virtual reality.Frontiers in psychology · 2023Article
- A face image classification method of autistic children based on the two-phase transfer learning.Frontiers in psychology · 2023Article
- Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study.JMIR pediatrics and parenting · 2022Article
- Application of Eye Tracking Technology in Medicine: A Bibliometric Analysis.Vision (Basel, Switzerland) · 2021Article
- 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
- Machine learning models improve prediction of large vessel occlusion and mechanical thrombectomy candidacy in acute ischemic stroke.Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism is a developmental condition currently identified by experts using observation, interview, and questionnaire techniques and primarily assessing social and communication deficits. Motor function and movement imitation are also altered in autism and can be measured more objectively. In this study, motion and eye tracking data from a movement imitation task were combined with supervised machine learning methods to classify 22 autistic and 22 non-autistic adults. The focus was on a reliable machine learning application. We have used nested validation to develop models and further tested the models with an independent data sample. Feature selection was aimed at selection stability to assure result interpretability. Our models predicted diagnosis with 73% accuracy from kinematic features, 70% accuracy from eye movement features and 78% accuracy from combined features. We further explored features which were most important for predictions to better understand movement imitation differences in autism. Consistent with the behavioural results, most discriminative features were from the experimental condition in which non-autistic individuals tended to successfully imitate unusual movement kinematics while autistic individuals tended to fail. Machine learning results show promise that future work could aid in the diagnosis process by providing quantitative tests to supplement current qualitative ones.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.