Predictive Control Implementation in Raspberry Pi for IoT-Based E-Health Incubator Systems

Abstract

This work details the deployment of a model predictive control approach on a Raspberry Pi-driven Internet of Things (IoT) architecture tailored for neonatal incubator supervision. The proposed system couples the Raspberry Pi with an E-health sensor suite to perform real-time data acquisition and regulation of essential physiological metrics, such as ambient temperature, skin temperature, and respiratory parameters. Utilizing the processing power of the Raspberry Pi alongside IoT communication capabilities, the platform enables healthcare professionals to remotely monitor infant conditions. The results confirm the viability of executing predictive control algorithms on low-power embedded devices and highlight the system's potential to improve the accuracy and responsiveness of thermal management in neonatal care settings.