Digital twin platform for neonatal incubator management

Abstract

This end of study project aims to create a digital twin for efficient neonatal incubator management. An ESP32 board is connected to environmental and biomedical sensors (temperature, humidity, oxygen, heart rate) to collect real-time data and sent it to Firebase. LSTM models are used to predict environmental conditions and infant comfort. An interactive 3D interface built with React and Blender allows intuitive data visualization. Test results demonstrate the proposed system’s effectiveness in enhancing neonatal care, particularly in resource-limited healthcare settings.