A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention Task - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

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

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-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 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).
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Dates and versions

hal-02324118 , version 1 (21-10-2019)

Identifiers

  • HAL Id : hal-02324118 , version 1

Cite

Jack Hadfield, Georgia Chalvatzaki, Petros Koutras, Mehdi Khamassi, Costas S Tzafestas, et al.. A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention Task. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), Nov 2019, Macau, China. ⟨hal-02324118⟩
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