用大模型模拟阿尔茨海默症患者语言特征，实现个性化助老机器人
MEMOR-E: In-Context and Fine-Tuned LLM Personalization for Alzheimer's Assistive Robotics
- 基于235例患者语音数据微调LLM，模拟不同阶段认知行为
- 通过上下文学习生成符合病情严重程度的错误摘要
- 输出可解释的文本，供照护者监督，提升人机信任
阿尔茨海默病导致记忆与语言能力逐步衰退，影响日常生活独立性，推动了社交助老机器人的发展。本文提出MEMOR-E，一款带交互平板的移动四足机器人，通过服药提醒、日常指导、记忆导向互动和陪伴支持患者与照护者。我们评估了微调大型语言模型（LLMs）以模拟阶段一致认知行为的可行性，并利用235例阿尔茨海默病患者语音转录文本及合成健康对照数据，分析其在标准神经心理学语言任务中的响应表现。同时研究了上下文学习（ICL）在LLMs中的应用，由第二模型生成领域与严重程度相关的认知错误摘要。结果表明，MEMOR-E能生成阶段感知、非诊断性的认知摘要，支持个性化辅助互动；可解释性AI机制将模型输出转化为透明、人类可读的证据，实现照护者监督，保障可信的人机交互。
原文摘要 · Abstract (English)
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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