# Modeling Engagement in Long-Term, In-Home Socially Assistive Robot Interventions for Children with Autism Spectrum Disorders

arXiv: https://arxiv.org/abs/2002.02453 · HTML: https://mlsanity.com/paper/2002.02453/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

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

- [A soft thumb-sized vision-based sensor with accurate all-round force perception](https://mlsanity.com/paper/2111.05934/) (2021)
- [MultiMediate'23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions](https://mlsanity.com/paper/2308.08256/) (2023)
- [Agent Teaming Situation Awareness (ATSA): A Situation Awareness Framework for Human-AI Teaming](https://mlsanity.com/paper/2308.16785/) (2023)
- [Evaluating Temporal Patterns in Applied Infant Affect Recognition](https://mlsanity.com/paper/2209.03496/) (2022)

## Abstract (arXiv)

Socially assistive robotics (SAR) has great potential to provide accessible, affordable, and personalized therapeutic interventions for children with autism spectrum disorders (ASD). However, human-robot 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 long-term, in-home SAR interventions for children with ASD. Specifically, we present two types of engagement models for each user: (i) generalized models trained on data from different users; and (ii) individualized models trained on an early subset of the user's 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 re-engagement actions at appropriate times. These results validate the feasibility and challenges of recognition and response to user disengagement in long-term, real-world HRI settings. The contributions of this work also inform the design of engaging and personalized HRI, especially for the ASD community.