ArticleJournal of autism and developmental disorders2022
Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms.
Article in Journal of autism and developmental disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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The trial behind it
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Who cites it
21 citing papers in PubMed.
- Neurobehavioral Assessment of Sensorimotor Function in Autism Using Smartphone Technology.Autism research : official journal of the International Society for Autism Research · 2026Article
- Deep learning based approach for Behavior classification in diagnoses of Autism Spectrum Disorder using naturalistic videos.Frontiers in computational neuroscience · 2026Article
- 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
- duet: An R package for dyadic analysis of motion data generated by OpenPose.Behavior research methods · 2025Article
- A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods.Journal of autism and developmental disorders · 2025Review
- The Impact of the COVID-19 Pandemic on Young Adults with Autism Spectrum Disorder: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms.Frontiers in physiology · 2025Article
- Continuous Monitoring of Head Turns: Compliance, Kinematics, and Reliability of Wearable Sensing.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Risk assessment and automatic identification of autistic children based on appearance.Scientific reports · 2024Article
- Detecting Autism from Head Movements using Kinesics.Proceedings of the ... ACM International Conference on Multimodal Interaction. ICMI (Conference) · 2024Article
- Autism Identification Based on the Intelligent Analysis of Facial Behaviors: An Approach Combining Coarse- and Fine-Grained Analysis.Children (Basel, Switzerland) · 2024Article
- Behavioral Intervention for Adults With Autism on Distribution of Attention in Triadic Conversations: A/B-Tested Pre-Post Study.JMIR formative research · 2024Article
- Decreased wrist rotation imitation abilities in children with autism spectrum disorder.Frontiers in psychiatry · 2024Article
- Enhancing early autism diagnosis through machine learning: Exploring raw motion data for classification.PloS one · 2024Article
- Modified Meta Heuristic BAT with ML Classifiers for Detection of Autism Spectrum Disorder.Biomolecules · 2023Article
- Automated anomalous child repetitive head movement identification through transformer networks.Physical and engineering sciences in medicine · 2023Article
- What are quantitative traits and how can they be used in autism research?Autism research : official journal of the International Society for Autism Research · 2023Article
- Predicting Autism from Head Movement Patterns during Naturalistic Social Interactions.Proceedings of the 2023 7th International Conference on Medical and Health Informatics (ICMHI 2023) : May 12-14, 2023, Kyoto, Japan. International Conference on Medical and Health Informatics (7th : 2023 : Kyoto, Japan) · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Our study investigated the feasibility of using head movement features to identify individuals with autism spectrum disorder (ASD). Children with ASD and typical development (TD) were required to answer ten yes-no questions, and they were encouraged to nod/shake head while doing so. The head rotation range (RR) and the amount of rotation per minute (ARPM) in the pitch (head nodding direction), yaw (head shaking direction) and roll (lateral head inclination) directions were computed, and further fed into machine learning classifiers as the input features. The maximum classification accuracy of 92.11% was achieved with the decision tree classifier with two features (i.e., RR_Pitch and ARPM_Yaw). Our study suggests that head movement dynamics contain objective biomarkers that could identify ASD.
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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.