Digital Twin Empowered Wireless Healthcare Monitoring for Smart Home

The dramatic progresses of wireless technologies and wearable devices have significantly promoted the development and popularity of smart home, while digital twin (DT) emerges as a game changer benefiting from its enhanced capabilities of visualization and interaction. The DT is able to build a real...

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Bibliographic Details
Published inIEEE Journal on Selected Areas in Communications Vol. 41; no. 11; p. 1
Main Authors Chen, Junxin, Wang, Wei, Fang, Bo, Liu, Yu, Yu, Keping, Leung, Victor C. M., Hu, Xiping
Format Journal Article
LanguageEnglish
Japanese
Published New York IEEE 01.11.2023
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Artificial intelligence
Biomedical monitoring
Cloud computing
Digital twin
Digital twins
Electrocardiography
Fall detection
Health care
Healthcare monitoring
Houses
Medical services
Monitoring
Real time
Smart buildings
Smart home
Smart homes
Smart houses
Telemedicine
Visualization
Wearable computers
Wearable technology
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Summary:The dramatic progresses of wireless technologies and wearable devices have significantly promoted the development and popularity of smart home, while digital twin (DT) emerges as a game changer benefiting from its enhanced capabilities of visualization and interaction. The DT is able to build a realtime and continuous visual replica of a physical object or process, and to provide realtime monitoring, anomaly prediction, smart interaction, and lifecycle management. This paper presents a DT model to empower healthcare monitoring in the smart home with the goals of graphical monitoring, healthcare prediction, and intelligent control. High fidelity DT of the house and its equipments is created for visualized monitoring, and two suites of devices are deployed for continuously acquiring the users' electrocardiograph (ECG) waves and the WiFi signals in the house. Two intelligent algorithms are then developed to perform fall detection from WiFi signals and to screen atrial fibrillation from ECG waves collected by wearable devices. Experimental results well validate the proposed model's effectiveness for smart home monitoring, and the advantages of the developed smart algorithms for healthcare prediction over counterparts.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2023.3310097