Establishing the Foundations of Emotional Intelligence of Care Companion Robots to Mitigate Agitation among High Risk Dementia Patients via Emphatic Patient-Robot Interactions (Preprint)

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. RESULTS The architecture development for the Personicle has been initiated with existing open source data and a non-Human Subject approval obtained in November 2023. A Community Advisory Board was formed and met in December 2023, and an ethical board was established with international colleagues. Full IRB was submitted in December 2023. CONCLUSIONS This innovative caregiving approach is designed to recognize signs of agitation, and upon recognition, intervene with empathic verbal communication. This proposal thus has the potential to have a significant impact on an emerging field of computational dementia science by reducing unnecessary agitation and falls of P-MSD, while improving caregiver burden.