An AI-Driven IoT and Data Analytics Framework for Intelligent Remote Patient Monitoring using Edge–Cloud Architecture

Abstract

The rapid growth of healthcare technologies and the increasing demand for continuous patient care have accelerated the adoption of Internet of Things (IoT)-based Remote Patient Monitoring (RPM) systems. Traditional healthcare monitoring approaches often require frequent hospital visits and are limited in providing real-time patient assessment, especially for elderly individuals and patients with chronic diseases. This study proposes an AI-driven IoT and Data Analytics framework for intelligent remote patient monitoring using an Edge–Cloud architecture. The proposed system integrates wearable sensors and IoT devices to continuously collect physiological parameters such as heart rate, blood pressure, body temperature, oxygen saturation (SpO₂), and electrocardiogram (ECG) signals. The collected healthcare data are transmitted through IoT gateways and processed using data preprocessing techniques including data cleaning, normalization, and feature extraction. Advanced machine learning and data analytics methods are employed to analyze patient health conditions and identify potential abnormalities in real time. Edge computing is incorporated to reduce latency and improve response time, while cloud infrastructure enables secure storage and large-scale healthcare data management. Furthermore, an alert mechanism is implemented to notify healthcare professionals and caregivers during critical health conditions. The proposed framework aims to enhance healthcare accessibility, improve early disease detection, reduce hospital workload, and support intelligent decision-making in remote healthcare environments. The system offers a scalable, efficient, and reliable solution for next-generation smart healthcare applications.