A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin

By Ying Liu, Lin Zhang, Yuan Yang, Longfei Zhou, Lei Ren, Fei Wang, Rong Liu, Zhibo Pang, M. Jamal Deen. IEEE Access, 2019

561 citations recorded by the source corpus.

With the development of technologies, such as big data, cloud computing, and the Internet of Things (IoT), digital twin is being applied in industry as a precision simulation technology from concept to practice. Further, simulation plays a very important role in the healthcare field, especially in research on medical pathway planning, medical resource allocation, medical activity prediction, etc. By combining digital twin and healthcare, there will be a new and efficient way to provide more accurate and fast services for elderly healthcare. However, how to achieve personal health management throughout the entire lifecycle of elderly patients, and how to converge the medical physical world and the virtual world to realize real smart healthcare, are still two key challenges in the era of precision medicine. In this paper, a framework of the cloud healthcare system is proposed based on digital twin healthcare (CloudDTH). This is a novel, generalized, and extensible framework in the cloud environment for monitoring, diagnosing and predicting aspects of the health of individuals using, for example, wearable medical devices, toward the goal of personal health management, especially for the elderly. CloudDTH aims to achieve interaction and convergence between medical physical and virtual spaces. Accordingly, a novel concept of digital twin healthcare (DTH) is proposed and discussed, and a DTH model is implemented. Next, a reference framework of CloudDTH based on DTH is constructed, and its key enabling technologies are explored. Finally, the feasibility of some application scenarios and a case study for real-time supervision are demonstrated.

Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/73e958090a9578114463461d95a7aab332afb05f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Zhong Fan
2. Aidan Fuller
3. Noël Crespi
4. Charles Day
5. G. Lee
6. Spiridon Bakiras
7. Roberto Minerva
8. Pan Hui

In Industrial and Manufacturing Engineering

1. Zhong Fan
2. Aidan Fuller
3. Noël Crespi
4. Charles Day
5. G. Lee
6. Mehmet Karamanoglu
7. Roberto Minerva

Citations in the corpus

• The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities (2021, background): “’s [56] models are generic ML-enabled frameworks for providing health services to elderly people.”
• Digital Twin: Enabling Technologies, Challenges and Open Research (2020, methodology): “Liu et al. [38] present a novel approach for the future delivery of healthcare combining cloud technology with Digital Twins to create a framework that helps to monitor, diagnose and predict the health of a patient.”
• All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda (2021, background): “real-time monitoring and crisis warning for older adults with their digital twins [247].”
• Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects (2022, background): “In the healthcare industry, the cloud represents a shared information platform between the medical service provider and the patients [26], while in the manufacturing domain it can serve as a common medium where enterprises can share data regarding the failure modes and maintenance needs of similar…”
• Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020, methodology): “The availability of sensor technologies as well as the growing communication and embedded processing power opens the path to the application of the Digital Twin concept in healthcare [216] [217][218].”
• Digital twins for health: a scoping review (2024, unknown): “The DT of the lungs expanded during the Covid pandemic and as part of Project BreathEasy, Onscale developed the DT of the lungs of COVID-19 patients to improve patient outcomes and optimize the use of limited ventilator resources, particularly in areas heavily affected by the outbreak 30 .”