Digital Twin for Intelligent Context-Aware IoT Healthcare Systems

被引:231
作者
Elayan, Haya [1 ]
Aloqaily, Moayad [2 ]
Guizani, Mohsen [2 ]
机构
[1] xAnalytics Inc, Res & Dev, Ottawa, ON K1N 6N5, Canada
[2] Qatar Univ, Dept Comp Sci & Engn, Doha, Qatar
关键词
Medical services; Monitoring; Internet of Things; Data models; Real-time systems; Databases; Machine learning; Digital twin (DT); electrocardiogram (ECG); Internet of Things (IoT); machine learning; smart healthcare%;
D O I
10.1109/JIOT.2021.3051158
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:16749 / 16757
页数:9
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