ArXiv

Language-Based Digital Twins for Elderly Cognitive Assistance

2026-06-25 · 17:45 UTC ·Mohammad Mehdi Hosseini, Mohammad H. Mahoor, Hiroko H. Dodge ·1 min read
Authors
Mohammad Mehdi Hosseini, Mohammad H. Mahoor, Hiroko H. Dodge
Categories
cs.AI
arXiv
https://arxiv.org/abs/2606.27334v1
PDF
https://arxiv.org/pdf/2606.27334v1

Brief

Language-based digital twins for elderly cognitive assistance propose a framework that leverages large language models, stylometric cues, and contextual metadata to mimic older adults' conversational behavior. The authors introduce a multi-head conditional variational autoencoder to measure reconstruction fidelity and predict MoCA scores. On the I-CONECT dataset the twins preserve identity-specific features, match real-data reconstruction and MoCA errors, and outperform GPT baselines.

Why it matters

Proposes a language-based digital twin framework that leverages large language models plus stylometric cues and contextual metadata, and introduces a multi-head conditional variational autoencoder (cVAE) to jointly measure reconstruction quality and predict cognitive (MoCA) scores.

Key details

  • On the I-CONECT dataset, the digital twin preserves identity-specific conversational characteristics, achieves reconstruction and MoCA prediction errors comparable to real data, and outperforms baseline GPT-generated responses; paper posted on arXiv 2026-06-25 and accepted at PETRA 2026.
Source evidence

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.

Comment: Accepted and published in the Proceedings of the ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA 2026). The final published version is available through the ACM Digital Library