Facial Emotion Recognition-based Engagement De-tection in Autism Spectrum Disorder

Abstract

Engagement is the state of alertness that a person experiences and the deliberate focus of their attention on a task-relevant stimulus. It positively correlates with many aspects such as learning, social support, and acceptance. Facial emotion recog-nition using artificial intelligence can be beneficial to automati-cally measure individual engagement especially when using au-tomated learning and playing modalities such as using Robots. In this study, we proposed an automatic engagement detection model through facial emotional recognition, particularly in de-termining autistic children’s engagement. The methodology em-ployed a transfer learning approach at the dataset level, utiliz-ing facial image datasets from typically developing (TD) chil-dren and children with ASD. The classification task was per-formed using convolutional neural network (CNN) methods. Comparative analysis revealed that the CNN method demon-strated superior accuracy compared to random forest (RF), support vector machine (SVM), and decision tree algorithms in both the TD and ASD datasets. The findings highlight the poten-tial of CNN-based facial emotion recognition for accurately assessing engagement in children with ASD, with implications for enhancing learning, social support, and acceptance in this population. This research contributes to the field of engagement measurement in autism and underscores the importance of lev-eraging AI techniques for improving understanding and support for children with ASD.