A comparison of machine learning techniques for modeling human-robot interaction with children with autism

Abstract

Several machine learning techniques are used to model the behavior of children with autism interacting with a humanoid robot, comparing a static model to a dynamic model using hand-coded features. Good accuracy (over 80%) is achieved in predicting child vocalizations; directions for future approaches to modeling the behavior of children with autism are suggested.