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）指一种能够以数字化方式高精度表征真实物理实体的前景技术。其典型优势在于：不仅能虚拟复现系统运行细节，还可分析当前状态、预测未来行为并优化控制策略。尽管DT已在智能制造、交通等领域广泛应用，但其传统范式局限于表征机器人、车辆等非生命实体。当应用于以人为核心的系统时，研究者提出了"人体数字孪生"（HDT）这一新概念。HDT可对人体进行数字化建模，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。这些特性使其在个性化医疗（PH）中展现出巨大应用潜力，可支持远程监测、诊断、处方、手术和康复等场景。然而，尽管前景广阔，HDT在不同层面仍面临重大研究挑战，近年已成为热点课题。本综述聚焦面向PH应用的HDT网络架构与关键技术：首先探讨HDT与传统DT的差异，阐释HDT的通用框架与核心功能；继而分析PH场景下的设计需求与挑战；随后概述包含数据采集层、数据通信层、计算层、数据管理层、数据分析与决策层的HDT网络架构；在详细评述实现该网络架构的关键技术后，总结HDT的未来研究方向。