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

arXiv: https://arxiv.org/abs/1812.00253 · HTML: https://mlsanity.com/paper/1812.00253/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2018.

## Built on by (most cited first, 4 of 4)

- [Automatic Context-Driven Inference of Engagement in HMI: A Survey](https://mlsanity.com/paper/2209.15370/) (2022)
- [Exploring Temporal Context and Human Movement Dynamics for Online Action Detection in Videos](https://mlsanity.com/paper/2106.13967/) (2021)
- [Occlusion-Robust Multi-Sensory Posture Estimation in Physical Human-Robot Interaction](https://mlsanity.com/paper/2208.06494/) (2022)
- [Graph Neural Network based Child Activity Recognition](https://mlsanity.com/paper/2212.09013/) (2022)

## Abstract (arXiv)

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).