This paper is in the following e-collection/theme issue:

New Methods (944) Augmented Reality (incl. Google Glass) Applications (90) Digital Biomarkers and Digital Phenotyping (191) Autism Spectrum Disorder (ASD) (109)

Published on in Vol 22, No 4 (2020): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13810, first published .
Toward Continuous Social Phenotyping: Analyzing Gaze Patterns in an Emotion Recognition Task for Children With Autism Through Wearable Smart Glasses

Toward Continuous Social Phenotyping: Analyzing Gaze Patterns in an Emotion Recognition Task for Children With Autism Through Wearable Smart Glasses

Toward Continuous Social Phenotyping: Analyzing Gaze Patterns in an Emotion Recognition Task for Children With Autism Through Wearable Smart Glasses

Authors of this article:

Anish Nag1 Author Orcid Image ;   Nick Haber2 Author Orcid Image ;   Catalin Voss3 Author Orcid Image ;   Serena Tamura4 Author Orcid Image ;   Jena Daniels5 Author Orcid Image ;   Jeffrey Ma6 Author Orcid Image ;   Bryan Chiang6 Author Orcid Image ;   Shasta Ramachandran6 Author Orcid Image ;   Jessey Schwartz6 Author Orcid Image ;   Terry Winograd3 Author Orcid Image ;   Carl Feinstein6 Author Orcid Image ;   Dennis P Wall6 Author Orcid Image
Article Authors Cited by (28) Tweetations (5) Metrics

Journals

  1. Berenguer C, Baixauli I, Gómez S, Andrés M, De Stasio S. 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 2020;17(17):6143 View
  2. Abbeduto L. Presidential Address, 2020—Using Technology to Deliver Services and Supports in Homes, Neighborhoods, and Communities: Evidence and Promise. Intellectual and Developmental Disabilities 2020;58(6):525 View
  3. Leblanc E, Washington P, Varma M, Dunlap K, Penev Y, Kline A, Wall D. Feature replacement methods enable reliable home video analysis for machine learning detection of autism. Scientific Reports 2020;10(1) View
  4. Oke I, VanderVeen D. Machine Learning Applications in Pediatric Ophthalmology. Seminars in Ophthalmology 2021;36(4):210 View
  5. Rashidan M, Sidek S, Yusof H, Khalid M, Dzulkarnain A, Ghazali A, Zabidi S, Sidique F. Technology-Assisted Emotion Recognition for Autism Spectrum Disorder (ASD) Children: A Systematic Literature Review. IEEE Access 2021;9:33638 View
  6. Kim D, Choi Y. Applications of Smart Glasses in Applied Sciences: A Systematic Review. Applied Sciences 2021;11(11):4956 View
  7. Li Y, Lu Q, Tao Q, Zhao X, Yu Y. SF-GAN: Face De-Identification Method Without Losing Facial Attribute Information. IEEE Signal Processing Letters 2021;28:1345 View
  8. Washington P, Chrisman B, Leblanc E, Dunlap K, Kline A, Mutlu C, Stockham N, Paskov K, Wall D. Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections. Intelligence-Based Medicine 2022;6:100056 View
  9. Kollias K, Syriopoulou-Delli C, Sarigiannidis P, Fragulis G. The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review. Electronics 2021;10(23):2982 View
  10. (previously Marzena Szkodo) M, Micai M, Caruso A, Fulceri F, Fazio M, Scattoni M. Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review. Neuroscience & Biobehavioral Reviews 2023;145:105021 View
  11. Washington P, Kalantarian H, Kent J, Husic A, Kline A, Leblanc E, Hou C, Mutlu O, Dunlap K, Penev Y, Varma M, Stockham N, Chrisman B, Paskov K, Sun M, Jung J, Voss C, Haber N, Wall D. Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study. JMIR Pediatrics and Parenting 2022;5(2):e26760 View
  12. Rother A, Spiliopoulou M. Virtual Reality for Medical Annotation Tasks: A Systematic Review. Frontiers in Virtual Reality 2022;3 View
  13. Deveau N, Washington P, Leblanc E, Husic A, Dunlap K, Penev Y, Kline A, Mutlu O, Wall D. Machine learning models using mobile game play accurately classify children with autism. Intelligence-Based Medicine 2022;6:100057 View
  14. Gong X, JosephNg P. Technology Behavior Model—Beyond Your Sight with Extended Reality in Surgery. Applied System Innovation 2022;5(2):35 View
  15. Wu X, Deng H, Jian S, Chen H, Li Q, Gong R, Wu J. Global trends and hotspots in the digital therapeutics of autism spectrum disorders: a bibliometric analysis from 2002 to 2022. Frontiers in Psychiatry 2023;14 View
  16. Mehralizadeh B, Baradaran B, Nikkhoo S, Soleiman P, Moradi H. A Sensorized Toy Car for Autism Screening Using Multi-Modal Features. Sustainability 2023;15(10):7790 View
  17. Asmetha Jeyarani R, Senthilkumar R. Eye Tracking Biomarkers for Autism Spectrum Disorder Detection using Machine Learning and Deep Learning Techniques: Review. Research in Autism Spectrum Disorders 2023;108:102228 View
  18. Germanese D, Colantonio S, Del Coco M, Carcagnì P, Leo M. Computer Vision Tasks for Ambient Intelligence in Children’s Health. Information 2023;14(10):548 View
  19. Washington P, Wall D. A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism. Annual Review of Biomedical Data Science 2023;6(1):211 View
  20. Kędras M, Sobecki J. What Is Hidden in Clear Sight and How to Find It—A Survey of the Integration of Artificial Intelligence and Eye Tracking. Information 2023;14(11):624 View
  21. Kargarandehkordi A, Kaisti M, Washington P. Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study. Applied Sciences 2024;14(4):1337 View
  22. Jiran Meitei A, Mohapatra B, Khundrakpam B, Tawfeeq Alee N, Chauhan G. Role of AI/ML in the Study of Autism Spectrum Disorders: A Bibliometric Analysis. Journal of Technology in Behavioral Science 2024 View
  23. Gao X, Yin L, Tian S, Huang Y, Ji Q. Wearable Technology for Signal Acquisition and Interactive Feedback in Autism Spectrum Disorder Intervention: A Review. IEEE Sensors Journal 2024;24(9):13797 View
  24. Qi S, Zhang S, Lin L, Li Y, Chen J, Ni Y, Du X, Zhang J, Ge P, Liu G, Wu J, Lin S, Gong M, Lin J, Chen L, He L, Lin D. Quantifying attention in children with intellectual and developmental disabilities through multicenter electrooculogram signal analysis. Scientific Reports 2024;14(1) View
  25. Liu Z, Li J, Zhang Y, Wu D, Huo Y, Yang J, Zhang M, Dong C, Jiang L, Sun R, Zhou R, Li F, Yu X, Zhu D, Guo Y, Chen J. Auxiliary Diagnosis of Children with Attention-Deficit/Hyperactivity Disorder: An Eye-Tracking Study with Novel Digital Biomarkers (Preprint). JMIR mHealth and uHealth 2024 View