MEMOR-E: In-Context and Fine-Tuned LLM Personalization for Alzheimer's Assistive Robotics
For Alzheimer's patients and caregivers, this work demonstrates the feasibility of LLM-based personalization in assistive robotics, though the results are preliminary with no quantitative performance metrics reported.
MEMOR-E is a mobile quadruped robot with an interactive tablet that assists Alzheimer's patients and caregivers through personalized interactions. Using fine-tuned LLMs on 235 patient transcriptions and in-context learning, it generates stage-aware cognitive summaries, achieving personalized assistive support with explainable AI for caregiver oversight.
Alzheimer's disease is a neurodegenerative disorder marked by progressive declines in memory and language that reduce independence in daily life, motivating socially assistive robotic support. This paper presents MEMOR-E, a mobile quadruped robot with an interactive tablet interface that assists patients and caregivers through medication reminders, routine guidance, memory oriented interactions, and companionship. We evaluated the feasibility of fine tuning large language models (LLMs) to emulate stage consistent cognitive behavior and interpret responses across standard neuropsychological language tasks, using audio transcriptions from 235 Alzheimer's patients and synthetically generated healthy controls. We also report findings on using in context learning (ICL) in LLMs, where a second LLM produced domain and severity level cognitive error summaries. Our results show that MEMOR-E can generate stage aware, non diagnostic cognitive summaries that support personalized assistive interactions, while explainable AI mechanisms translate model outputs into transparent, human readable evidence to enable caregiver oversight and trustworthy human robot interaction.