Distinguishing Engagement Facets: An Essential Component for AI-based Healthcare
Year: 2021
Type: preprint
Abstract: Engagement in Human-Machine Interaction is the process by which entitiesparticipating in the interaction establish, maintain, and end their perceived
connection. It is essential to monitor the engagement state of patients in
various AI-based 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 multifaceted
construct which is composed of behavioral, emotional, and mental components.
Previous research has neglected the multi-faceted nature of engagement. 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. A highest accuracy of 74.57% with an F-Score and mean
absolute error of 0.74 and 0.23 respectively was obtained on a balanced dataset
of 22242 instances with neural network-based classification.
Source: arXiv (Cornell University)
Author: Hanan Salam
Language: English
Topic: Emotion and Mood Recognition
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3217214500