MEMOR-E: In-Context and Fine-Tuned LLM Personalization for Alzheimer's Assistive Robotics
Quick Take
MEMOR-E is a mobile quadruped robot designed to assist Alzheimer's patients through personalized interactions using fine-tuned large language models (LLMs). It employs in-context learning to generate cognitive summaries, enhancing caregiver oversight and interaction trustworthiness.
Key Points
- MEMOR-E provides medication reminders and routine guidance for Alzheimer's patients.
- The robot uses audio transcriptions from 235 patients to fine-tune LLMs.
- In-context learning enables generation of cognitive error summaries by a second LLM.
- The system supports personalized interactions while ensuring explainable AI mechanisms.
- Results indicate improved caregiver oversight and trustworthy human-robot interactions.
Article Content
From source RSS / original summaryarXiv:2605. 23941v1 Announce Type: new Abstract: 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.
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