A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention Task
Jack Hadfield, Georgia Chalvatzaki, Petros Koutras, Mehdi Khamassi, Costas S. Tzafestas, Petros Maragos
Abstract
In this work, we tackle the problem of child engagement estimation while children freely interact with a robot in a friendly, room-like environment. We propose a deep learning-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 regularly in the room, fuse the results and feed them to a deep Neural Network (NN) 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 Child Robot Interaction (CRI) tasks both for Typically Developed (TD) children and children affected by autism (ASD).
BibTeX
@inproceedings{iros2019_adeeplearningapp,
title = {A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention Task},
author = {Jack Hadfield and Georgia Chalvatzaki and Petros Koutras and Mehdi Khamassi and Costas S. Tzafestas and Petros Maragos},
booktitle = {IROS 2019},
year = {2019}
}