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