IoT-enabled decision-aided system for remote monitoring of patients towards smart healthcare

The rapid growth of Internet of Things (IoT) technologies has significantly transformed smart healthcare systems by enabling continuous monitoring of physiological parameters through wearable devices. However, many existing healthcare monitoring systems rely heavily on cloud-based architectures, which lead to increased network latency, high energy consumption, and limited real-time decision support. This study proposes an IoT-enabled Smart Healthcare Decision-aid System (ISHDS) that integrates a wearable physical sensor node (WPSN), mobile-based Edge AI computing, and cloud-assisted data analysis for efficient real-time health monitoring. The proposed system is designed as a four-layer architecture consisting of a wearable sensing layer, mobile edge processing layer, communication layer, and cloud analytics layer. A prototype WPSN device is developed to continuously monitor multiple physiological parameters, including blood pressure, body temperature, heart rate, blood oxygen saturation ( S p O 2 ) and respiration rate. A pilot experimental study was conducted for 5 consecutive days involving 27 participants aged between 11 and 77 years with a combination of healthy and patients having chronic conditions to evaluate system performance. The primary outcomes included measurement accuracy, system latency, and real-time decision support capability. The collected data were processed using an on-device data fusion approach through a mobile application (Healthy App), which reduces network latency and communication overhead while improving system responsiveness. Experimental results suggest that the proposed framework achieves high accuracy (up to 99.96%) in physiological data analysis; however, this result is preliminary and requires further validation on larger and more diverse populations. These findings indicate that the proposed ISHDS framework offers an efficient and scalable solution for smart healthcare applications by combining wearable sensing, Edge AI-based data processing, and intelligent data fusion. The system shows strong potential for deployment in remote healthcare monitoring and real-time medical decision support environments.