Research Progress and Prospects of Intelligent Diabetes Monitoring Systems: A Review
Yuanyuan Zou, Zhengkang Chu, Tongyan Yang, Jinhong Guo, Diangeng Li
Abstract
A comprehensive architecture for an intelligent diabetes monitoring system, covering various components and their technologies is presented. Core aspects are systematically outlined, including advancements in glucose sensors using biofluidics and a comparison of communication standards. Bluetooth Low Energy (BLE) and narrowband Internet of Things (IoT) are chosen for short-range and remote communication. We explore cloud computing’s efficiency in data processing, noting drawbacks like high energy use and latency. Edge computing is considered for these challenges, reducing reliance on remote servers. Emphasizing artificial intelligence’s (AI) role in data analysis and modeling, we highlight its benefits for adaptable monitoring outcomes. From our analysis of glucose biosensors, communication standards, cloud tech, and AI, we outline applications for intelligent glucose monitoring systems. Notable achievements and challenges are summarized, guiding future research. Our review aims to aid technologists and medical professionals in understanding such systems, offering guidance for diabetes care enhancement. Ultimately, we strive to advance diabetes diagnosis, treatment, and quality of life using intelligent medical technologies.