Cyber-Physical Wireless Networks for Smart Health Monitoring for Elderly Persons

The rapid advancement in Cyber-Physical Systems (CPS) and wireless networking technologies has ushered in a new era for healthcare, particularly in the domain of smart health monitoring for elderly populations. This paper explores the development and implementation of a state-of-the-art cyber-physical wireless network system designed to enhance the health monitoring of elderly individuals, thereby improving their quality of life and enabling independent living. By leveraging the interconnectivity of CPS within a 6G wireless network framework, our proposed system facilitates real-time health monitoring and emergency response through the seamless integration of wearable health sensors, mobile devices, and cloud computing services. The core of our system architecture is built on robust wireless communication protocols that ensure reliable data transmission from wearable sensors to the central monitoring system. This paper introduces a digital twin (DT)-based model designed to enhance healthcare monitoring within smart homes, aiming at graphical monitoring, healthcare prediction, and intelligent control. A high-fidelity DT of the home and its equipment is developed for visual monitoring purposes, and two sets of devices are utilised to continually gather users’ electrocardiograph (ECG) waves and the WiFi signals within the home. Furthermore, two advanced deep learning algorithms are proposed for detecting falls using WiFi signals and identifying atrial fibrillation from ECG waves recorded by wearable devices. The effectiveness of the proposed model for smart home monitoring is confirmed through experimental results, showcasing the superiority of the developed intelligent algorithms in healthcare prediction compared to existing solutions.