A Deep Learning Approach for MultiView Engagement Estimation of Children in a ChildRobot Joint Attention Task In this work, we tackle the problem of child engagement estimation while children freely interact with a robot in a friendly, roomlike environment. We propose a deep learningbased multiview solution that takes advantage of recent developments in human pose detection. We extract the childxe2x80x99s pose from different RGBD 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 childxe2x80x99s attitude, interest and attention while cooperating with a robot. The goal is to integrate this model in nextgeneration 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).

Modeling Engagement in LongTerm, InHome Socially Assistive Robot Interventions for Children with Autism Spectrum Disorders. Socially assistive robotics (SAR) has great potential to provide accessible, affordable, and personalized therapeutic interventions for children with autism spectrum disorders (ASD). However, humanrobot interaction (HRI) methods are still limited in their ability to autonomously recognize and respond to behavioral cues, especially in atypical users and everyday settings. This work applies supervised machine learning algorithms to model user engagement in the context of longterm, inhome SAR interventions for children with ASD. Specifically, two types of engagement models are presented for each user: 1) generalized models trained on data from different users; and 2) individualized models trained on an early subset of the useru0027s data. The models achieved approximately 90% accuracy (AUROC) for post hoc binary classification of engagement, despite the high variance in data observed across users, sessions, and engagement states. Moreover, temporal patterns in model predictions could be used to reliably initiate reengagement actions at appropriate times. These results validate the feasibility and challenges of recognition and response to user disengagement in longterm, realworld HRI settings. The contributions of this work also inform the design of engaging and personalized HRI, especially for the ASD community.