Digital Twin for Intelligent Context-Aware IoT Healthcare Systems
المؤلف | Elayan, Haya |
المؤلف | Aloqaily, Moayad |
المؤلف | Guizani, Mohsen |
تاريخ الإتاحة | 2022-10-27T08:40:01Z |
تاريخ النشر | 2021-12-01 |
اسم المنشور | IEEE Internet of Things Journal |
المعرّف | http://dx.doi.org/10.1109/JIOT.2021.3051158 |
الاقتباس | Elayan, H., Aloqaily, M., & Guizani, M. (2021). Digital twin for intelligent context-aware IoT healthcare systems. IEEE Internet of Things Journal, 8(23), 16749-16757. |
الملخص | 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. |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | Digital twin (DT) electrocardiogram (ECG) Internet of Things (IoT) machine learning smart healthcare% |
النوع | Article |
الصفحات | 16749-16757 |
رقم العدد | 23 |
رقم المجلد | 8 |
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