A Digital Twin Approach of A-vent Wireless Sensor for Real-Time and Predictive Monitoring of Patient Ventilator Asynchrony

• Main Authors: Oppus, Carlos M, Santiago, Paul Ryan A., Torres, Justin Bryce M., Mercado, Neil Angelo M., Cabacungan, Paul M., Cao, Reymond P., Cabacungan, Nerissa G., Tangonan, Gregory L
• Format: text
• Published: Archīum Ateneo 2023
• Subjects: Biomedical Sensor; Digital Twin; Healthcare; Internet of Medical Things; TinyML; Biomedical; Electrical and Computer Engineering; Engineering
• Online Access: https://archium.ateneo.edu/ecce-faculty-pubs/153; https://doi.org/10.1109/ECAI58194.2023.10194042
• Institution: Ateneo De Manila University

Prior to the recent work on a low-cost Ateneo mechanical ventilator machine named A-vent, this study demonstrated a simple Digital Twin approach for a real-time monitoring system that can be useful to any mechanical ventilator unit. Previous research concentrated on A-vent design, Near Cloud data caching, and Machine Learning model development. However, it lacks Internet of Things capabilities for remote monitoring applications. This work incorporates new software components to a Near Cloud server that stores and monitors the ventilator and patient data across the wireless network. Wireless sensor nodes attached to the A-vent and patient interaction model capture the time-series waveform of the ventilator, its predictive analysis, and oximeter values. The data queries command displays the data stored in the Near Cloud databases on the monitoring dashboard. It shows a digital representation of the system, allowing real-time updates to be viewed remotely and easily comprehended.