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

Formative Evaluation of Digital Health Interventions (2491) New Methods (963) Machine Learning (1501) Autism Spectrum Disorder (ASD) (112) Mobile Health (mhealth) (2982) mHealth for Wellness, Behavior Change and Prevention (2922) Diagnostic Tools in Mental Health (248) Imaging Informatics (200)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39917, first published .
Training and Profiling a Pediatric Facial Expression Classifier for Children on Mobile Devices: Machine Learning Study

Training and Profiling a Pediatric Facial Expression Classifier for Children on Mobile Devices: Machine Learning Study

Training and Profiling a Pediatric Facial Expression Classifier for Children on Mobile Devices: Machine Learning Study

Authors of this article:

Agnik Banerjee1 Author Orcid Image ;   Onur Cezmi Mutlu2 Author Orcid Image ;   Aaron Kline1 Author Orcid Image ;   Saimourya Surabhi1 Author Orcid Image ;   Peter Washington3 Author Orcid Image ;   Dennis Paul Wall1, 4, 5 Author Orcid Image
Article Authors Cited by (9) Tweetations (2) Metrics

Journals

  1. Gau M, Ting H, Toh T, Wong P, Woo P, Wo S, Tan G. Effectiveness of Using Artificial Intelligence for Early Child Development Screening. Green Intelligent Systems and Applications 2023;3(1):1 View
  2. Jaiswal A, Washington P. Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study. JMIR Formative Research 2024;8:e52660 View
  3. Sun Y, Kargarandehkordi A, Slade C, Jaiswal A, Busch G, Guerrero A, Phillips K, Washington P. Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study. JMIR Research Protocols 2024;13:e46493 View
  4. Završnik J, Kokol P, Žlahtič B, Blažun Vošner H. Artificial Intelligence and Pediatrics: Synthetic Knowledge Synthesis. Electronics 2024;13(3):512 View
  5. Kargarandehkordi A, Kaisti M, Washington P. Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study. Applied Sciences 2024;14(4):1337 View
  6. Zhu Q, Zhuang H, Zhao M, Xu S, Meng R. A study on expression recognition based on improved mobilenetV2 network. Scientific Reports 2024;14(1) View
  7. Han B, Chang Y, Tan R, Han C. Evaluating deep learning techniques for identifying tongue features in subthreshold depression: a prospective observational study. Frontiers in Psychiatry 2024;15 View

Books/Policy Documents

  1. Kushwaha N, Singh B. Proceedings of International Joint Conference on Advances in Computational Intelligence. View