Abstract
This chapter is from the forthcoming The Oxford Handbook of Affective Computing edited by Rafael Calvo, Sidney K. D'Mello, Jonathan Gratch, and Arvid Kappas. Affective computing can illuminate early emotional dynamics and provide tools for intervention in disordered emotional functioning. This chapter reviews affective computing approaches to understanding emotional communication in typically developing children and children with an autism spectrum disorder (ASD). It covers the application of automated measurement of the dynamics of emotional expression and discusses advances in the modeling of infant and parent interactions based on insights from time-series analysis, machine learning, and recurrence theory. The authors discuss progress in the automated measurement of vocalization in infants and children and new methods for the efficient measurement of sympathetic activation and its application in children with ASD. They conclude by presenting translational applications of affective computing to children with ASD, including the use of embodied conversational agents (ECAs) to understand and influence the affective dynamics of learning, and the use of robots to improve the social and emotional functioning of children with ASD.