Elaboration of Innovative Digital Twin Models for Healthcare Monitoring With 6G Functionalities

Abstract

Remote monitoring of individuals with special healthcare needs and controlling their living spaces using emerging technologies is a significant focus for researchers from various disciplines, forming a crucial element of future healthcare development. Digitizing the healthcare sector demands expertise and knowledge transfer to create new paradigms and innovative solutions to enhance life quality and reduce healthcare burdens. One of the most promising technologies in this area is the Digital Twin (DT), a virtual replica of the real world with advanced features for data clustering, classification, and forecasting. This paper introduces an innovative context-aware framework for monitoring indoor air quality and human activity, integrating technologies like the Internet of Things (IoT), 6G networks, sensing and localization techniques, Edge Computing, Deep Learning models, and cloud platforms. The multidisciplinary research emphasizes the interaction of the DT concept with its environment and other technologies. The contributions include: establishing an architecture with sensors, gateways, and a DT object on Azure cloud, validated with AI models; linking 6G network sensing and communication capabilities with IoT-based techniques to enhance performance; and developing deep learning models for Human Activity Recognition (HAR) using inertial sensors, achieving a test accuracy of 99.34% and a real-time accuracy of 92.10%.

Keywords

• IMU sensors
• deep learning
• edge computing
• 6G
• terahertz frequency
• IoT