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Introducing the Monitoring Equipment Mask Environment
Filter face masks are Respiratory Protective Equipment designed to protect the wearer from various hazards, suit various health situations, and match the specific requirements of the wearer. Current traditional face masks have several limitations. In this paper, we present (ME) [Formula: see text] ,...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460738/ https://www.ncbi.nlm.nih.gov/pubmed/36080824 http://dx.doi.org/10.3390/s22176365 |
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author | Pazienza, Andrea Monte, Daniele |
author_facet | Pazienza, Andrea Monte, Daniele |
author_sort | Pazienza, Andrea |
collection | PubMed |
description | Filter face masks are Respiratory Protective Equipment designed to protect the wearer from various hazards, suit various health situations, and match the specific requirements of the wearer. Current traditional face masks have several limitations. In this paper, we present (ME) [Formula: see text] , the Monitoring Equipment Mask Environment: an innovative reusable 3D-printed eco-sustainable mask with an interchangeable filter. (ME) [Formula: see text] is equipped with multiple vital sensors on board, connected to a system-on-a-chip micro-controller with computational capabilities, Bluetooth communication, and a rechargeable battery that allows continuous monitoring of the wearer’s vital signs. It monitors body temperature, heart rate, and oxygen saturation in a non-invasive, strategically positioned way. (ME) [Formula: see text] is accompanied by a mobile application that provides users’ health information. Furthermore, through Edge Computing Artificial Intelligence (Edge AI) modules, it is possible to detect an abnormal and early symptoms linked to possible pathologies, possibly linked to the respiratory or cardiovascular tract, and therefore perform predictive analysis, launch alerts, and recommendations. To validate the feasibility of embedded in-app Edge AI modules, we tested a machine learning model able to distinguish COVID-19 versus seasonal influenza using only vital signs. By generating new synthetic data, we confirm the highly reliable performances of such a model, with an accuracy of [Formula: see text]. |
format | Online Article Text |
id | pubmed-9460738 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94607382022-09-10 Introducing the Monitoring Equipment Mask Environment Pazienza, Andrea Monte, Daniele Sensors (Basel) Article Filter face masks are Respiratory Protective Equipment designed to protect the wearer from various hazards, suit various health situations, and match the specific requirements of the wearer. Current traditional face masks have several limitations. In this paper, we present (ME) [Formula: see text] , the Monitoring Equipment Mask Environment: an innovative reusable 3D-printed eco-sustainable mask with an interchangeable filter. (ME) [Formula: see text] is equipped with multiple vital sensors on board, connected to a system-on-a-chip micro-controller with computational capabilities, Bluetooth communication, and a rechargeable battery that allows continuous monitoring of the wearer’s vital signs. It monitors body temperature, heart rate, and oxygen saturation in a non-invasive, strategically positioned way. (ME) [Formula: see text] is accompanied by a mobile application that provides users’ health information. Furthermore, through Edge Computing Artificial Intelligence (Edge AI) modules, it is possible to detect an abnormal and early symptoms linked to possible pathologies, possibly linked to the respiratory or cardiovascular tract, and therefore perform predictive analysis, launch alerts, and recommendations. To validate the feasibility of embedded in-app Edge AI modules, we tested a machine learning model able to distinguish COVID-19 versus seasonal influenza using only vital signs. By generating new synthetic data, we confirm the highly reliable performances of such a model, with an accuracy of [Formula: see text]. MDPI 2022-08-24 /pmc/articles/PMC9460738/ /pubmed/36080824 http://dx.doi.org/10.3390/s22176365 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pazienza, Andrea Monte, Daniele Introducing the Monitoring Equipment Mask Environment |
title | Introducing the Monitoring Equipment Mask Environment |
title_full | Introducing the Monitoring Equipment Mask Environment |
title_fullStr | Introducing the Monitoring Equipment Mask Environment |
title_full_unstemmed | Introducing the Monitoring Equipment Mask Environment |
title_short | Introducing the Monitoring Equipment Mask Environment |
title_sort | introducing the monitoring equipment mask environment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460738/ https://www.ncbi.nlm.nih.gov/pubmed/36080824 http://dx.doi.org/10.3390/s22176365 |
work_keys_str_mv | AT pazienzaandrea introducingthemonitoringequipmentmaskenvironment AT montedaniele introducingthemonitoringequipmentmaskenvironment |