Published on in Vol 7, No 4 (2020): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13174, first published .
The Performance of Emotion Classifiers for Children With Parent-Reported Autism: Quantitative Feasibility Study

The Performance of Emotion Classifiers for Children With Parent-Reported Autism: Quantitative Feasibility Study

The Performance of Emotion Classifiers for Children With Parent-Reported Autism: Quantitative Feasibility Study

Journals

  1. Nag A, Haber N, Voss C, Tamura S, Daniels J, Ma J, Chiang B, Ramachandran S, Schwartz J, Winograd T, Feinstein C, Wall D. Toward Continuous Social Phenotyping: Analyzing Gaze Patterns in an Emotion Recognition Task for Children With Autism Through Wearable Smart Glasses. Journal of Medical Internet Research 2020;22(4):e13810 View
  2. Washington P, Leblanc E, Dunlap K, Penev Y, Kline A, Paskov K, Sun M, Chrisman B, Stockham N, Varma M, Voss C, Haber N, Wall D. Precision Telemedicine through Crowdsourced Machine Learning: Testing Variability of Crowd Workers for Video-Based Autism Feature Recognition. Journal of Personalized Medicine 2020;10(3):86 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. Abououf M, Otrok H, Mizouni R, Singh S, Damiani E. How Artificial Intelligence and Mobile Crowd Sourcing are Inextricably Intertwined. IEEE Network 2021;35(3):252 View