Language-Based Digital Twins for Elderly Cognitive Assistance

2026-06-25

AI Summary

This study addresses the challenge of early detection of mild cognitive impairment (MCI) in older adults, hindered by the lack of non-invasive, personalized, and continuous monitoring tools. The authors propose the first language-based digital twin framework that leverages large language models to simulate individual conversational behavior by integrating stylistic features with contextual metadata, thereby constructing a personalized cognitive health model. The core innovation lies in a novel multi-head conditional variational autoencoder designed to jointly optimize identity fidelity and cognitive consistency. Experiments on the I-CONECT dataset demonstrate that the proposed method matches real conversations in terms of speaker identity preservation, dialogue reconstruction quality, and MoCA score prediction accuracy, significantly outperforming baseline generative approaches such as GPT.

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, we propose a language-based digital twin framework that leverages large language models (LLMs) to mimic the conversational behavior of elderly individuals by incorporating stylometric cues and contextual metadata. To evaluate fidelity and cognitive consistency, we introduce a multi-head conditional variational autoencoder (cVAE) that jointly measures reconstruction quality and predicts cognitive scores. Experiments on the I-CONECT dataset show that the digital twin preserves identity-specific characteristics and achieves reconstruction and MoCA prediction errors comparable to real data, while outperforming baseline GPT-generated responses. These results highlight the potential of language-based digital twins as a scalable and non-invasive approach for personalized and continuous cognitive health monitoring.

Problem

• Mild Cognitive Impairment
• language-based biomarkers
• digital twins
• cognitive health monitoring
• elderly

Innovation

• language-based digital twin
• large language models
• stylometric cues
• conditional variational autoencoder
• cognitive health monitoring