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

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. Categories and Subject Descriptors

• Type: Journal
• Year: 2011
• Where: HRI
• Authors: Elaine Short, David Feil-Seifer, Maja J. Mataric
• Keywords: Children With Autism, HRI 2011, Human Computer Interaction, Humanoid Robot, Static Model