An Edge-Enabled IoT Healthcare Monitoring Framework Using Raspberry Pi Pico W and Cloud-Based Physiological Data Management

Vollständiger Abstract

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The rapid growth of the Internet of Medical Things (IoMT) has created significant opportunities for developing intelligent remote healthcare systems capable of continuous patient monitoring. However, many existing low-cost healthcare monitoring solutions are limited by single-parameter sensing, poor interoperability, inadequate cloud integration, and restricted scalability for multi-patient applications. This paper presents the design and development of an edge-enabled IoT-based smart healthcare monitoring platform using the Raspberry Pi Pico W for real-time acquisition, processing, and cloud storage of physiological data. The proposed system integrates three biomedical sensors, namely the MAX30102 sensor for heart rate and blood oxygen (SpO₂) monitoring, the DS18B20 digital sensor for body temperature measurement, and the AD8232 analog front-end for electrocardiogram (ECG) signal acquisition. The embedded edge node performs local signal preprocessing, physiological parameter extraction, timestamp synchronization, and secure wireless transmission of patient data to a cloud database through REST-based communication over Wi-Fi. A cloud-centric architecture is developed using Supabase to provide reliable data storage, multi-node scalability, and seamless integration with future intelligent healthcare applications. The proposed framework supports continuous remote patient monitoring while maintaining a low hardware cost, reduced computational overhead, and efficient data communication. Experimental implementation demonstrates reliable real-time physiological data acquisition, stable cloud synchronization, and effective management of multimodal healthcare data suitable for subsequent analytical and predictive applications. The developed platform establishes a scalable foundation for next-generation smart healthcare systems by combining low-cost embedded hardware, edge computing, and cloud technologies to improve accessibility, reliability, and remote medical monitoring.