Distinguishing Engagement Facets: An Essential Component for AI-based Interactive Healthcare

TL;DR

This paper introduces a system to distinguish behavioral, emotional, and mental engagement facets in AI-based healthcare interactions, using machine learning to enable more nuanced understanding of patient engagement.

Contribution

It presents a novel framework for recognizing multiple engagement facets in healthcare, addressing the previous focus on binary engagement detection.

Findings

Achieved an F-Score of 0.74 with neural networks

Compared various machine learning classifiers

Provided a baseline for future engagement facet recognition research

Abstract

Engagement in Human-Machine Interaction is the process by which entities participating in the interaction establish, maintain, and end their perceived connection. It is essential to monitor the engagement state of patients in various AI-based interactive healthcare paradigms. This includes medical conditions that alter social behavior such as Autism Spectrum Disorder (ASD) or Attention-Deficit/Hyperactivity Disorder (ADHD). Engagement is a multi-faceted construct which is composed of behavioral, emotional, and mental components. Previous research has neglected this multi-faceted nature of engagement and focused on the detection of engagement level or binary engagement label. In this paper, a system is presented to distinguish these facets using contextual and relational features. This can facilitate further fine-grained analysis. Several machine learning classifiers including traditional and deep learning models are compared for this task. An F-Score of 0.74 was obtained on a balanced dataset of 22242 instances with neural network-based classification. The proposed framework shall serve as a baseline for further research on engagement facets recognition, and its integration is socially assistive robotic applications.