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.
| Comments: | 8 pages 14 figures |
| Subjects: | Artificial Intelligence (cs.AI); Robotics (cs.RO) |
| MSC classes: | 68T50, 68T05, 68T40 |
| ACM classes: | I.2.9; H.5.2; I.2.7; I.2.6 |
| Cite as: | arXiv:2605.23941 [cs.AI] |
| (or arXiv:2605.23941v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23941 arXiv-issued DOI via DataCite |
From: Maissa Smaili Miss [view email]
[v1]
Tue, 28 Apr 2026 18:59:03 UTC (2,780 KB)
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