Establishing the Foundations of Emotional Intelligence in Care Companion Robots to Mitigate Agitation Among High-Risk Patients With Dementia: Protocol for an Empathetic Patient-Robot Interaction Study

Abstract

Background: There are an estimated 6.7 million persons living with dementia in the U.S.; expected to double by 2060. Those at highest risk, persons experiencing moderate to severe dementia (P-MSD), are 4-5 times more likely to fall than those without dementia, as they often experience unpredictable agitation, leading to unsteady gait. Socially assistive robots fail to address the dynamically changing emotional states associated with agitation, and there is a lack of understanding how emotional states change, how they impact agitation and gait over time, and how social robots can best respond by showing empathy.Objective: Design and validate a foundational model of emotional intelligence for empathic patient-robot interaction that mitigates agitation among those at highest risk, P-MSD.Methods: A design science approach will be used to: 1) collect and store granular, personal, chronological data (Personicle), using real-time visual, audio and physiological sensing technologies in a simulation lab and Board & Care facilities; 2) develop statistical models to understand and forecast a P-MSD's emotional state, agitation level and gait in real-time using ML/AI and the Personicle; 3) design and test an empathy-focused conversation model, focused on storytelling; and 4) test and evaluate the empathy-focused conversation model for the Care Companion Robot (CCR) in the community.