IoT-Driven Experimental Framework for Advancing Electrical Impedance Tomography

Abstract

This research paper focuses on the current emphasis on the latest industrial revolution, particularly the innovative integration of artificial intelligence and the Internet of Things (IoT). The study explores the seamless integration of Electrical Impedance Tomography (EIT) with IoT, presenting a groundbreaking framework where impedance-based sensing plays a vital role in enhancing the dynamic and adaptable qualities of IoT ecosystems. This contribution facilitates intelligent decision-making and real-time monitoring. The research investigates the application of non-invasive Electrical Impedance Tomography for the rapid identification of minor changes in the electrical impedance of the body or a simulated object. Electrodes positioned at the ends of the phantom’s cylinder measure impedance changes through the application of a high-frequency, low-current signal. Image reconstruction employs both forward and inverse solutions, utilizing a triangular finite element method (FEM) mesh to determine conductivity distribution based on recommended phantom models. The integration of IoT enables data capture, enhancing accessibility through remote monitoring. The novel IoT system proves advantageous for various engineering research applications, providing easily monitored parameters in both commercial and clinical contexts.