Design and Evaluation of Patient Monitoring Systems Using Wearable Devices: Enhancing Healthcare with Advanced Technology

Abstract

This study explores the development and evaluation of patient monitoring systems in hospitals using wearable devices. These systems utilize advanced technologies such as the Internet of Things (IoT), biosensors, and machine learning algorithms to provide continuous monitoring of patients' vital signs. Wearable devices are lightweight, portable, and non-invasive, making them suitable for hospital environments. By enabling real-time data collection and processing, these devices allow healthcare professionals to respond promptly to critical changes in patients' conditions. The research methodology focuses on designing an integrated system that combines wearable sensors with IoT-enabled data transmission and machine learning for predictive analysis. The system was tested on a sample group of patients under controlled conditions to assess its accuracy, reliability, and scalability. Results showed a significant improvement in detecting abnormal health conditions, achieving a 98% detection accuracy and reducing the need for manual monitoring by 60%. This research highlights the transformative potential of wearable-based monitoring systems in improving hospital workflows, reducing operational costs, and enhancing patient outcomes. The study also identifies challenges related to data privacy, interoperability, and scalability, proposing solutions for future implementation. Ultimately, the findings underscore the importance of adopting wearable technology in modern healthcare to address the growing demand for efficient, real-time patient monitoring systems.