AI-Enabled IoT Architecture for Continuous Remote Patient Health Monitoring

Abstract

Continuous remote patient monitoring (CRPM) combines wearable/ambient sensors, Internet of Things (IoT) connectivity, and artificial intelligence (AI) to deliver timely, personalized healthcare outside clinical settings. This paper proposes a layered, secure, and scalable AI-enabled IoT architecture for CRPM that integrates on-device edge intelligence, privacy-preserving federated learning, and cloud analytics with clinician dashboards and automated alerting. We describe hardware/software components, data flows, AI model choices for real-time anomaly detection and prognosis, and security/privacy mechanisms (encryption, access control, and optional blockchain anchoring). We present an evaluation plan using public physiological datasets (MIMIC-IV, PhysioNet waveforms) and wearable data, describe performance metrics (latency, accuracy, false alarm rate, energy), and discuss deployment, regulatory, and ethical considerations. The architecture aims to reduce hospital readmissions, enable early detection of deterioration, and improve chronic disease management while safeguarding patient data.

Keywords: Remote patient monitoring, Internet of Things, wearable sensors, edge AI, federated learning, security, MIMIC, PhysioNet