TY - JOUR
T1 - Digital Twin Empowered Wireless Healthcare Monitoring for Smart Home
AU - Chen, Junxin
AU - Wang, Wei
AU - Fang, Bo
AU - Liu, Yu
AU - Yu, Keping
AU - Leung, Victor C.M.
AU - Hu, Xiping
N1 - Publisher Copyright:
© 1983-2012 IEEE.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - 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.
AB - 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.
KW - Digital twin
KW - artificial intelligence
KW - healthcare monitoring
KW - smart home
UR - http://www.scopus.com/inward/record.url?scp=85169675511&partnerID=8YFLogxK
U2 - 10.1109/JSAC.2023.3310097
DO - 10.1109/JSAC.2023.3310097
M3 - Article
AN - SCOPUS:85169675511
SN - 0733-8716
VL - 41
SP - 3662
EP - 3676
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
IS - 11
ER -