Digital twin (DT), refers to a promising technique to digitally and accurately represent actual physical entities. One typical advantage of DT is that it can be used to not only virtually replicate a system's detailed operations but also analyze the current condition, predict future behaviour, and refine the control optimization. Although DT has been widely implemented in various fields, such as smart manufacturing and transportation, its conventional paradigm is limited to embody non-living entities, e.g., robots and vehicles. When adopted in human-centric systems, a novel concept, called human digital twin (HDT) has thus been proposed. Particularly, HDT allows in silico representation of individual human body with the ability to dynamically reflect molecular status, physiological status, emotional and psychological status, as well as lifestyle evolutions. These prompt the expected application of HDT in personalized healthcare (PH), which can facilitate remote monitoring, diagnosis, prescription, surgery and rehabilitation. However, despite the large potential, HDT faces substantial research challenges in different aspects, and becomes an increasingly popular topic recently. In this survey, with a specific focus on the networking architecture and key technologies for HDT in PH applications, we first discuss the differences between HDT and conventional DTs, followed by the universal framework and essential functions of HDT. We then analyze its design requirements and challenges in PH applications. After that, we provide an overview of the networking architecture of HDT, including data acquisition layer, data communication layer, computation layer, data management layer and data analysis and decision making layer. Besides reviewing the key technologies for implementing such networking architecture in detail, we conclude this survey by presenting future research directions of HDT.

翻译：数字孪生（DT）是一种能够以数字方式精准表示真实物理实体的前沿技术。其典型优势在于不仅能够虚拟复现系统的详细运行过程，还可用于分析当前状态、预测未来行为并优化控制策略。尽管DT已在智能制造、交通运输等领域得到广泛应用，但其传统范式仅局限于表征非生命实体（如机器人和车辆）。当应用于以人为中心的系统时，由此衍生出称为人类数字孪生（HDT）的新概念。具体而言，HDT能够实现对个体人体的数字表征，动态反映分子状态、生理状态、情绪心理状态及生活方式演变，从而推动其在个性化医疗（PH）领域的预期应用——涵盖远程监测、诊断、处方、手术及康复等场景。然而，尽管潜力巨大，HDT在不同方面仍面临重大研究挑战，近年来已成为热门课题。本综述聚焦个性化医疗中HDT的网络架构与关键技术，首先探讨HDT与传统DT的差异，继而阐述HDT的通用框架与核心功能，分析其在PH应用中的设计需求与挑战。在此基础上，我们系统梳理HDT的网络架构，包括数据采集层、数据通信层、计算层、数据管理层以及数据分析与决策层，并深入评述实现该网络架构所需的关键技术。最后，通过展望HDT的未来研究方向为本文作结。