One size does not fit all: detecting attention in children with autism using machine learning

Abstract

Detecting the attention of children with autism spectrum disorder (ASD) is of paramount importance for desired learning outcome. Teachers often use subjective methods to assess the attention of children with ASD, and this approach is tedious and inefficient due to disparate attentional behavior in ASD. This study explores the attentional behavior of children with ASD and the control group: typically developing (TD) children, by leveraging machine learning and unobtrusive technologies such as webcams and eye-tracking devices to detect attention objectively. Person-specific and generalized machine models for face-based, gaze-based, and hybrid-based (face and gaze) are proposed in this paper. The performances of these three models were compared, and the gaze-based model outperformed the others. Also, the person-specific model achieves higher predictive power than the generalized model for the ASD group. These findings stress the direction of model design from traditional one-size-fits-all models to personalized models.