Digital Twin for Intelligent Context-Aware IoT Healthcare Systems

By Haya Elayan, Moayad Aloqaily, Mohsen Guizani. IEEE Internet of Things Journal, 2021

332 citations recorded by the source corpus.

Since the emergence of digital and smart healthcare, the world has hastened to apply various technologies in this field to promote better health operation and patients’ well being, increase life expectancy, and reduce healthcare costs. One promising technology and game changer in this domain is digital twin (DT). DT is expected to change the concept of digital healthcare and take this field to another level that has never been seen before. DT is a virtual replica of a physical asset that reflects the current status through real-time transformed data. This article proposes and implements an intelligent context-aware healthcare system using the DT framework. This framework is a beneficial contribution to digital healthcare and to improve healthcare operations. Accordingly, an electrocardiogram (ECG) heart rhythms classifier model was built using machine learning to diagnose heart disease and detect heart problems. The implemented models successfully predicted a particular heart condition with high accuracy in different algorithms. The collected results have shown that integrating DT with the healthcare field would improve healthcare processes by bringing patients and healthcare professionals together in an intelligent, comprehensive, and scalable health ecosystem. Also, implementing an ECG classifier that detects heart conditions gives the inspiration for applying ML and artificial intelligence with different human body metrics for continuous monitoring and abnormalities detection. Finally, neural-network-based algorithms deal better with ECG data than traditional ML algorithms.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/1afa1751e0e4670399f6004ef519d311132ba44d

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In Industrial and Manufacturing Engineering

1. Zhen Hu
2. Zhu Han
3. Sankaran Mahadevan

Citations in the corpus

• Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2023, methodology): “[91] Smart healthcare Patients Handling data for analysis and developing AI models Improving healthcare operations”
• A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, methodology): “ML models, which can learn from a dataset and be used to predict on new, unseen data, have become a popular method for modeling physical systems in a digital twin (Min et al., 2019; Ibrahim et al., 2020; Liu et al., 2020a; Priyanka et al., 2022; Elayan et al., 2021; Ladj et al., 2021; Hu et al., 202”
• Artificial Intelligence for the Metaverse: A Survey (2022, methodology): “In [122], a data-drivenbased DT framework (see Fig.”