ArticleJournal of medical Internet research2020
Toward Continuous Social Phenotyping: Analyzing Gaze Patterns in an Emotion Recognition Task for Children With Autism Through Wearable Smart Glasses.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Emotion recognition and human-robot interaction: a systematic review of methods and applications.Frontiers in robotics and AI · 2026Pooled it
- Scoping review of precision child and youth mental health research: dwelling in possibility.Frontiers in psychiatry · 2025Pooled it
- Exploring the Impact of Augmented Reality in Children and Adolescents with Autism Spectrum Disorder: A Systematic Review.International journal of environmental research and public health · 2020Pooled it
- Home-based eye tracking for early autism screening: a scoping review of approaches, evidence, and implementation challenges.BMC psychiatry · 2026Article
- AI-Enabled Smart Glasses for Active Aging: Scoping Review.JMIR aging · 2026Review
- 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
- Non-Invasive Wearables in Pediatric Healthcare: A Comprehensive Review of Uses and Implications.Children (Basel, Switzerland) · 2025Review
- Auxiliary Diagnosis of Children With Attention-Deficit/Hyperactivity Disorder Using Eye-Tracking and Digital Biomarkers: Case-Control Study.JMIR mHealth and uHealth · 2024Article
- Quantifying attention in children with intellectual and developmental disabilities through multicenter electrooculogram signal analysis.Scientific reports · 2024Article
- Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study.Applied sciences (Basel, Switzerland) · 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
- Global trends and hotspots in the digital therapeutics of autism spectrum disorders: a bibliometric analysis from 2002 to 2022.Frontiers in psychiatry · 2023Article
- Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study.JMIR pediatrics and parenting · 2022Article
- Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections.Intelligence-based medicine · 2022Article
- Machine learning models using mobile game play accurately classify children with autism.Intelligence-based medicine · 2022Article
- Selection of trustworthy crowd workers for telemedical diagnosis of pediatric autism spectrum disorder.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2021Article
- Feature replacement methods enable reliable home video analysis for machine learning detection of autism.Scientific reports · 2020Article
- Presidential Address, 2020-Using Technology to Deliver Services and Supports in Homes, Neighborhoods, and Communities: Evidence and Promise.Intellectual and developmental disabilities · 2020Review
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundSeveral studies have shown that facial attention differs in children with autism. Measuring eye gaze and emotion recognition in children with autism is challenging, as standard clinical assessments must be delivered in clinical settings by a trained clinician. Wearable technologies may be able to bring eye gaze and emotion recognition into natural social interactions and settings.
objectiveThis study aimed to test: (1) the feasibility of tracking gaze using wearable smart glasses during a facial expression recognition task and (2) the ability of these gaze-tracking data, together with facial expression recognition responses, to distinguish children with autism from neurotypical controls (NCs).
methodsWe compared the eye gaze and emotion recognition patterns of 16 children with autism spectrum disorder (ASD) and 17 children without ASD via wearable smart glasses fitted with a custom eye tracker. Children identified static facial expressions of images presented on a computer screen along with nonsocial distractors while wearing Google Glass and the eye tracker. Faces were presented in three trials, during one of which children received feedback in the form of the correct classification. We employed hybrid human-labeling and computer vision-enabled methods for pupil tracking and world-gaze translation calibration. We analyzed the impact of gaze and emotion recognition features in a prediction task aiming to distinguish children with ASD from NC participants.
resultsGaze and emotion recognition patterns enabled the training of a classifier that distinguished ASD and NC groups. However, it was unable to significantly outperform other classifiers that used only age and gender features, suggesting that further work is necessary to disentangle these effects.
conclusionsAlthough wearable smart glasses show promise in identifying subtle differences in gaze tracking and emotion recognition patterns in children with and without ASD, the present form factor and data do not allow for these differences to be reliably exploited by machine learning systems. Resolving these challenges will be an important step toward continuous tracking of the ASD phenotype.
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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.