Predicting Core Characteristics of ASD Through Facial Emotion Recognition and Eye Tracking in Youth

Is visual gaze in children with autism spectrum disorder related to sequence of emotion intensity presentation? An eye‐tracking study of natural emotion perception processes

“…Based on the stability of gaze reductions during emotion perception in children with ASD (Reimann et al, 2021), some machine learning methods (e.g., deep neural networks, random forest repressors, etc.) combined with the eye movements features during emotion recognition tasks have been used to identify individuals with ASD and achieved relatively accredited classification accuracy (Jiang et al, 2019; Jiang et al, 2020). Machine learning methods can be more efficient and objective than observation of clinical symptoms and ratings according to different observation scales.…”

The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review

“…Specifically, in [36], emotion recognition task played the most important role in revealing differences between ASD and TD participants. Additionally, a high prediction accuracy was achieved in [30] regarding social impairment and restricted, repetitive, and stereotyped behaviours and interests. On the other hand, although there was a high classification accuracy in [32], there were no differences between ASD and TD participants when emotion recognition accuracy was taken into account.…”

“…Searches on the database were carried out by the first author and aimed at identifying English, full-text articles, published after 2015, utilising the following Boolean string ((((eye-tracking) OR (gaze)) OR (eye movement)) AND (autism)) AND (Machine Learning). Studies complying with the following inclusion criteria were selected: (a) patient groups had an ASD diagnosis, although there were also participants with ASD and ASD+ADHD diagnosis in [30]; (b) control groups consisted of TD participants apart from one study with Low/Medium/High ASD risk and ASD participants only [31]; (c) participants' ages ranged from toddlers to adults; (d) the aim of the studies was ASD detection using machine learning combined with eye-tracking technology. PRISMA recommendations concerning how to avoid the risk of bias were considered.…”

“…A high classification accuracy (86%) was achieved. In [30] the authors carried out one more eye-tracking and emotion recognition study and created a Random Forest regressor to predict an SRS-2 score [33] measuring social impairment and RBS-R score [34] measuring restricted, repetitive, and stereotyped behaviours and interests. ASD, ADHD, ASD+ADHD and TD participants were recruited.…”