# Are you still with me? Continuous Engagement Assessment from a Robot's Point of View

arXiv: https://arxiv.org/abs/2001.03515 · HTML: https://mlsanity.com/paper/2001.03515/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2020.

## Built on by (most cited first, 12 of 12)

- [Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues](https://mlsanity.com/paper/2404.01986/) (2024)
- [Automatic Context-Driven Inference of Engagement in HMI: A Survey](https://mlsanity.com/paper/2209.15370/) (2022)
- [Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactions](https://mlsanity.com/paper/2203.10518/) (2022)
- [Multipar-T: Multiparty-Transformer for Capturing Contingent Behaviors in Group Conversations](https://mlsanity.com/paper/2304.12204/) (2023)
- [HIINT: Historical, Intra- and Inter- personal Dynamics Modeling with Cross-person Memory Transformer](https://mlsanity.com/paper/2305.12369/) (2023)
- [MINT-RVAE: Multi-Cues Intention Prediction of Human-Robot Interaction using Human Pose and Emotion Information from RGB-only Camera Data](https://mlsanity.com/paper/2509.22573/) (2025)
- [Real-Time Human-Robot Interaction Intent Detection Using RGB-based Pose and Emotion Cues with Cross-Camera Model Generalization](https://mlsanity.com/paper/2512.17958/) (2025)
- [HUI360: A 360deg Egocentric Dataset and Baselines for Human-Robot Interaction Anticipation](https://mlsanity.com/paper/2608.11051/) (2026)
- [Distinguishing Engagement Facets: An Essential Component for AI-based Interactive Healthcare](https://mlsanity.com/paper/2111.11138/) (2021)
- [User Experience Estimation in Human-Robot Interaction Via Multi-Instance Learning of Multimodal Social Signals](https://mlsanity.com/paper/2507.23544/) (2025)
- [DS-HGCN: A Dual-Stream Hypergraph Convolutional Network for Predicting Student Engagement via Social Contagion](https://mlsanity.com/paper/2512.20059/) (2025)
- [Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset](https://mlsanity.com/paper/2606.03694/) (2026)

## Abstract (arXiv)

Continuously measuring the engagement of users with a robot in a Human-Robot Interaction (HRI) setting paves the way towards in-situ reinforcement learning, improve metrics of interaction quality, and can guide interaction design and behaviour optimisation. However, engagement is often considered very multi-faceted and difficult to capture in a workable and generic computational model that can serve as an overall measure of engagement. Building upon the intuitive ways humans successfully can assess situation for a degree of engagement when they see it, we propose a novel regression model (utilising CNN and LSTM networks) enabling robots to compute a single scalar engagement during interactions with humans from standard video streams, obtained from the point of view of an interacting robot. The model is based on a long-term dataset from an autonomous tour guide robot deployed in a public museum, with continuous annotation of a numeric engagement assessment by three independent coders. We show that this model not only can predict engagement very well in our own application domain but show its successful transfer to an entirely different dataset (with different tasks, environment, camera, robot and people). The trained model and the software is available to the HRI community as a tool to measure engagement in a variety of settings.