Wearable Wireless Sensors for Healthcare Monitoring: A Review

Abstract

 As the population grows, limited capabilities constrain medical institutions in some countries. This makes it hard to monitor patients' conditions promptly and comprehensively, potentially causing missed optimal treatment opportunities. Thus, researching wearable wireless sensor-based health monitoring systems becomes particularly important. These systems can give medical institutions more intuitive, accurate, real-time data on human health, aiding disease diagnosis. This study systematically analyzes and summarizes relevant literature to examine traditional healthcare sensors' drawbacks, such as poor comfort, high costs, and reliance on fixed environments. It also explains the basic principles of three commonly used wireless sensors in health monitoring. Furthermore, to address resource optimization in these systems, the study develops three innovative algorithms: an improved BFGS quasi-Newton method-based energy-optimal allocation algorithm, a window-based rate control algorithm (w-RCA), and a priority-based task scheduling and resource allocation mechanism (PTS-RA). Empirical validation demonstrates that these algorithms significantly enhance the overall performance of healthcare monitoring networks, providing robust technical support for 5G-enabled telemedicine applications. The study also offers practical cases and technical references for using sensors in scientific fields.