ENGAGEMENT RECOGNITION OF CHILDREN WITH AUTISM

Abstract

Social robots are becoming popular in therapy sessions of children with autism because it helps to develop children’s learning abilities. For example, robots encourage children to repeat actions it demonstrates like high-five, handshake, hugging, singing song, dancing, that positively influence on children’s treatment. However, robots cannot perceive children’s engagement to make interaction more natural. In this work our goal is to build machine learning models that could predict the child’s engagement with higher accuracy. We expect to train model that could recognize children engagement based on multi-modal behavioral cues: facial expression, body movement that were captured from video.