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

Authors

Carlos M. Oppus, Ateneo de Manila UniversityFollow
Paul Ryan A. Santiago, Ateneo de Manila UniversityFollow
Justin Bryce M. Torres, Ateneo de Manila UniversityFollow
Neil Angelo M. Mercado, Ateneo de Manila University
Paul M. Cabacungan, Ateneo de Manila UniversityFollow
Reymond P. Cao, Ateneo de Manila UniversityFollow
Nerissa G. Cabacungan, Ateneo de Manila UniversityFollow
Gregory L. Tangonan, Ateneo de Manila UniversityFollow

Document Type

Conference Proceeding

Publication Date

1-1-2023

Abstract

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.

Recommended Citation

C. M. Oppus et al., "A Digital Twin Approach of A-vent Wireless Sensor for Real-Time and Predictive Monitoring of Patient Ventilator Asynchrony," 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Bucharest, Romania, 2023, pp. 1-5, doi: 10.1109/ECAI58194.2023.10194042.