Promoting Cognitive Health in Elder Care with Large Language Model-Powered Socially Assistive Robots

Abstract

As the global population ages, there is increasing need for accessible technologies that promote cognitive health and detect early signs of cognitive decline. This research demonstrates the potential for in-residence monitoring and assessment of cognitive health using large language model (LLM)-powered socially assistive robots (SARs). We conducted a 5-week within-subjects study involving 22 older adults in retirement homes to investigate the feasibility of large language model (LLM)-powered socially assistive robots (SARs) for promoting and assessing cognitive health. We designed tasks that involved verbal dialogue based on clinically validated cognitive tools. Our findings reveal improved task performance after three robot-administered sessions, with significantly more detailed picture descriptions, fewer word repetitions in semantic fluency, and reduced need for hints. We found that older adults were more socially engaged in robot-administered tasks compared to those administered by a human, and they accepted and were willing to engage with socially assistive robots (SARs) in this context, which had not been tested before.