Smart Healthcare Monitoring through Edge Intelligence

Abstract

Digital healthcare technologies have significantly improved patient monitoring and disease prediction; however, conventional cloud-based healthcare systems face challenges such as communication latency, bandwidth consumption, privacy concerns, and delayed clinical responses. This study proposes a Smart Healthcare Monitoring through Edge Intelligence framework that integrates Artificial Intelligence (AI), Edge Computing, the Internet of Things (IoT), and Machine Learning (ML) to enable real-time and secure healthcare services. The proposed architecture employs wearable sensors to continuously monitor vital physiological parameters, including heart rate, ECG, blood oxygen saturation (SpO₂), body temperature, blood pressure, respiratory rate, glucose level, and physical activity. Medical data is processed locally at edge devices for noise removal, anomaly detection, risk assessment, and selective cloud synchronization. AI-based edge analytics support early disease prediction, personalized healthcare recommendations, and rapid emergency alerts while reducing communication overhead and protecting patient privacy. The proposed framework demonstrates improved monitoring accuracy, lower response latency, enhanced network efficiency, better data security, and reliable healthcare decision-making. It provides a scalable and intelligent solution for telemedicine, remote patient monitoring, and next-generation smart healthcare systems.