ZigBee Based Low Latency IoT and AI Integrated Framework for Real Time Telehealth Monitoring

Basant Kumar (Department of Mathematics and Computer Science, Modern College of Business and Science, Bowshar, Muscat)
Mohammad Shahnawaz Shaikh (2) Faculty of Engineering and Technology, Marwadi University, Rajkot 360003, India. 3) Lincoln University College, 47301, Petaling Jaya, Selangor Darul Ehsan)

Emerging Science Journal
Vol. 10 No. 2 (2026): April

The Internet of Things (IoT) and Artificial Intelligence (AI) have opened up new frontiers in remote health monitoring with the integration of technologies and transformative solutions in order to detect real-time health monitoring and disparities. This article shows an innovative and integrated wireless health surveillance system, which is aimed at auxiliary environments, especially for elderly and chronically ill patients. The system links IoT sensors to monitor heart rate, body temperature, and oxygen level with cloud-based AI-driven systems for continuous real-time health monitoring of data shared by IoT sensors. Taking advantage of the ZigBee protocol for low-power, reliable communication ensures spontaneous data transmission from a system wearer to a centralized processing unit. At the most basic level, the system uses advanced machine learning algorithms such as random forest, support vector machine (SVM), and logistic regression to identify health discrepancies with a high degree of accuracy. The random forest model in particular gets an impressive 95% accuracy and recalls 100%, ensuring reliable detection of minimum false negatives and important health issues. The modular structure of the system allows for the addition of more sensors, including blood pressure and glucose monitors, to ensure scalability and adaptability to suit the varying needs of different patients. In a real-world care facility, strict testing was carried out on the capability of monitoring the capacity system with just a 120 ms delay and a power consumption of 3.8 mW/h, which made it very suitable for long-term, energy-skilled deployment. By addressing some of the major issues such as high delays, false alarms, and lack of integration in current systems, this research provides a scalable, reliable, and user-friendly solution for telehealth. The proposed system not only adds more accuracy and freedom to the patient in the clinical setting but also lessens the burden of the healthcare providers, paving the way to a new generation of intelligent health solutions.