Design and Deployment of an Azure-Powered Edge-Cloud Biomedical Monitoring System

The purpose of the article is to develop and validate a hybrid biomedical signal monitoring system that integrates edge-computing capabilities with Microsoft Azure cloud services. The article describes a new edge-and-cloud architecture based on the EmotiBit wearable sensor and a Raspberry Pi gateway, enabling continuous acquisition, local buffering, and scalable cloud synchronization of multi-modal biometric signals. Using hardware evaluation (signal accuracy, power consumption, wireless connectivity), Azure Blob Storage, Azure SQL Database, Power BI dashboards, and Azure Stream Analytics, the authors demonstrate a seamless pipeline for real-time visualization and post-hoc analysis of electrocardiogram (ECG), photoplethysmogram (PPG), galvanic skin response (GSR), and additional vital signs. We illustrate the proposed system by conducting a rigorous battery performance analysis under three workloads continuous sensing alone, sensing with local storage, and combined local plus cloud upload and comparing operational endurance across battery capacities ranging from 1,200 mAh (≈12 h runtime) to 6,000 mAh (≈45 h runtime). Our proposal allows improvement in data delivery reliability to ≥99.5 % under network latencies up to 200 ms and reduces baseline clinical monitoring load by up to 30 %. The new method for performance evaluation is confirmed by quantitative runtime and reliability calculations. New research results supplement existing telemedicine paradigms by demonstrating enhanced patient mobility, near-real-time anomaly detection, and scalable data management, and can be used for remote patient monitoring, clinical diagnostics, and large-scale health studies. This paper is novel because it jointly optimizes edge-device design, battery endurance, and cloud analytics within a unified, deployable Azure framework.