[Paper Review] Networking Architecture and Key Supporting Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey

Jiayuan Chen, Changyan Yi+4 more|arXiv (Cornell University)|Jan 10, 2023

Digital Transformation in Industry234 references11 citations

TL;DR

This paper is a comprehensive survey of the networking architecture and key technologies enabling human digital twins (HDT) in personalized healthcare (PH), detailing a five-layer HDT networking framework and discussing design requirements, challenges, and future directions.

ABSTRACT

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.

Motivation & Objective

• Define how HDT differs from conventional DT and why HDT is needed for PH.
• Present a universal five-layer HDT networking architecture.
• Identify design requirements and challenges for HDT in PH applications.
• Survey key technologies supporting each HDT layer (data acquisition, communication, computation, data management, data analysis).
• Suggest future research directions to advance HDT in PH.

Proposed method

• Review and synthesize HDT literature to establish differences from conventional DT.
• Propose and describe a universal HDT framework across five layers.
• Analyze design requirements and challenges in PH contexts.
• Survey enabling technologies for each HDT layer (sensing, networking, AI, edge/cloud, security).
• Contrast HDT with existing DT surveys and provide guiding insights for researchers.

Experimental results

Research questions

• RQ1 What distinguishes HDT from conventional DT in healthcare settings?
• RQ2 What is the universal HDT networking architecture and its five layers?
• RQ3 What are the design requirements and challenges for HDT in PH applications?
• RQ4 What enabling technologies support each HDT layer and how should they be integrated?
• RQ5 What future research directions can advance HDT for PH?

Key findings

• HDT differs from conventional DT in emotion/psychology, data heterogeneity, ethics, mobility, and data complexity.
• A five-layer HDT networking architecture is proposed: data acquisition, communication, computation, data management, and data analysis/decision making.
• HDt requires data with high quality, real-time, multi-source, multi-modal characteristics and robust data management frameworks.
• xURLLC with ultra-low latency, reliability, and high bandwidth is essential, motivating integration with deterministic networking like TSN.
• Ultra-low RTT and real-time feedback are critical for immersive and interactive HDT applications, including haptic and XR-enabled scenarios.
• Privacy, security, and data integrity are paramount due to highly sensitive healthcare data.