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

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 ca...

Full description

Saved in:
Bibliographic Details
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:
Online Access:https://archium.ateneo.edu/ecce-faculty-pubs/153
https://doi.org/10.1109/ECAI58194.2023.10194042
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Ateneo De Manila University
id ph-ateneo-arc.ecce-faculty-pubs-1147
record_format eprints
spelling ph-ateneo-arc.ecce-faculty-pubs-11472024-02-21T06:36:25Z A Digital Twin Approach of A-vent Wireless Sensor for Real-Time and Predictive Monitoring of Patient Ventilator Asynchrony 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 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. 2023-01-01T08:00:00Z text https://archium.ateneo.edu/ecce-faculty-pubs/153 https://doi.org/10.1109/ECAI58194.2023.10194042 Electronics, Computer, and Communications Engineering Faculty Publications Archīum Ateneo Biomedical Sensor Digital Twin Healthcare Internet of Medical Things TinyML Biomedical Electrical and Computer Engineering Engineering
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic Biomedical Sensor
Digital Twin
Healthcare
Internet of Medical Things
TinyML
Biomedical
Electrical and Computer Engineering
Engineering
spellingShingle Biomedical Sensor
Digital Twin
Healthcare
Internet of Medical Things
TinyML
Biomedical
Electrical and Computer Engineering
Engineering
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
A Digital Twin Approach of A-vent Wireless Sensor for Real-Time and Predictive Monitoring of Patient Ventilator Asynchrony
description 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.
format text
author 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
author_facet 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
author_sort Oppus, Carlos M
title A Digital Twin Approach of A-vent Wireless Sensor for Real-Time and Predictive Monitoring of Patient Ventilator Asynchrony
title_short A Digital Twin Approach of A-vent Wireless Sensor for Real-Time and Predictive Monitoring of Patient Ventilator Asynchrony
title_full A Digital Twin Approach of A-vent Wireless Sensor for Real-Time and Predictive Monitoring of Patient Ventilator Asynchrony
title_fullStr A Digital Twin Approach of A-vent Wireless Sensor for Real-Time and Predictive Monitoring of Patient Ventilator Asynchrony
title_full_unstemmed A Digital Twin Approach of A-vent Wireless Sensor for Real-Time and Predictive Monitoring of Patient Ventilator Asynchrony
title_sort digital twin approach of a-vent wireless sensor for real-time and predictive monitoring of patient ventilator asynchrony
publisher Archīum Ateneo
publishDate 2023
url https://archium.ateneo.edu/ecce-faculty-pubs/153
https://doi.org/10.1109/ECAI58194.2023.10194042
_version_ 1792202642245550080