  In this work we tackle the problem of child engagement estimation while
children freely interact with a robot in their room. We propose a deep-based
multi-view solution that takes advantage of recent developments in human pose
detection. We extract the child's pose from different RGB-D cameras placed
elegantly in the room, fuse the results and feed them to a deep neural network
trained for classifying engagement levels. The deep network contains a
recurrent layer, in order to exploit the rich temporal information contained in
the pose data. The resulting method outperforms a number of baseline
classifiers, and provides a promising tool for better automatic understanding
of 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 an
attention monitoring tool during various CRI tasks both for Typically Developed
(TD) children and children affected by autism (ASD).
