基于语言的数字孪生用于老年人认知辅助
Language-Based Digital Twins for Elderly Cognitive Assistance
AI总结：
提出基于大语言模型的数字孪生框架，通过风格测量和上下文元数据模拟老年人对话行为，并引入多条件变分自编码器评估保真度和认知一致性，实现非侵入式认知健康监测。
摘要：
数字孪生已成为个性化医疗保健的一种有前景的范式，能够对个体行为和健康轨迹进行建模。在认知健康领域，轻度认知障碍（MCI）的早期检测仍然具有挑战性，而语言和对话模式可作为非侵入性生物标志物。在这项工作中，我们提出了一个基于语言的数字孪生框架，利用大型语言模型（LLMs）通过融入风格测量线索和上下文元数据来模拟老年人的对话行为。为了评估保真度和认知一致性，我们引入了一个多头条件变分自编码器（cVAE），该编码器联合测量重建质量并预测认知评分。在I-CONECT数据集上的实验表明，数字孪生保留了身份特定特征，并实现了与真实数据相当的重建和MoCA预测误差，同时优于基线GPT生成的响应。这些结果凸显了基于语言的数字孪生作为一种可扩展且非侵入性的方法，在个性化、持续的认知健康监测方面的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
