Language-Based Digital Twins for Elderly Cognitive Assistance

Paper Overview

• Research Area: Machine Learning (ML)
• Authors: Mohammad Mehdi Hosseini, Mohammad H. Mahoor, Hiroko H. Dodge
• Published: 2026-06-27
• arXiv: 2606.27334

Abstract

Digital twins have emerged as a promising paradigm for personalized healthcare, enabling modeling of individual behavior and health trajectories. In cognitive health, early detection of Mild Cognitive Impairment (MCI) remains challenging, where language and conversational patterns serve as non-invasive biomarkers. In this work, the authors propose a language-based digital twin framework that leverages large language models (LLMs) to mimic the conversational behavior of elderly individuals by incorporating their personal language patterns. The framework aims to simulate individual conversational behavior to support early detection of MCI and provide cognitive assistance for the elderly.

Key Points

• Applies the digital twin concept to cognitive healthcare, modeling individual behavior and health trajectories.
• Targets early detection of Mild Cognitive Impairment (MCI), a task that remains challenging in practice.
• Uses language and conversational patterns as non-invasive biomarkers.
• Leverages LLMs to reproduce an elderly individual's conversational behavior based on their personal language patterns.

> For the full abstract and paper details, see the arXiv page.

*Auto-collected on 2026-06-27.*