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In this work we tackle the problem of child engagement estimation whilechildren freely interact with a robot in their room. We propose a deep-basedmulti-view solution that takes advantage of recent developments in human posedetection. We extract the child's pose from different RGB-D cameras placedelegantly in the room, fuse the results and feed them to a deep neural networktrained for classifying engagement levels. The deep network contains arecurrent layer, in order to exploit the rich temporal information contained inthe pose data. The resulting method outperforms a number of baselineclassifiers, and provides a promising tool for better automatic understandingof a child's attitude, interest and attention while cooperating with a robot.The goal is to integrate this model in next generation social robots as anattention monitoring tool during various CRI tasks both for Typically Developed(TD) children and children affected by autism (ASD).

A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention task