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Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation

Home monitoring supports the continuous improvement of the therapy by sharing data with healthcare professionals. It is required when life-threatening events can still occur after hospital discharge such as neonatal apnea. However, multiple sources of external noise could affect data quality and/or...

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Autores principales: Foresti, Ruben, Statello, Rosario, Delmonte, Nicola, Lo Muzio, Francesco Paolo, Rozzi, Giacomo, Miragoli, Michele, Sarli, Leopoldo, Ferrari, Gianluigi, Macaluso, Claudio, Maggio, Marcello Giuseppe, Pisani, Francesco, Costantino, Cosimo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749724/
https://www.ncbi.nlm.nih.gov/pubmed/35009792
http://dx.doi.org/10.3390/s22010249
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author Foresti, Ruben
Statello, Rosario
Delmonte, Nicola
Lo Muzio, Francesco Paolo
Rozzi, Giacomo
Miragoli, Michele
Sarli, Leopoldo
Ferrari, Gianluigi
Macaluso, Claudio
Maggio, Marcello Giuseppe
Pisani, Francesco
Costantino, Cosimo
author_facet Foresti, Ruben
Statello, Rosario
Delmonte, Nicola
Lo Muzio, Francesco Paolo
Rozzi, Giacomo
Miragoli, Michele
Sarli, Leopoldo
Ferrari, Gianluigi
Macaluso, Claudio
Maggio, Marcello Giuseppe
Pisani, Francesco
Costantino, Cosimo
author_sort Foresti, Ruben
collection PubMed
description Home monitoring supports the continuous improvement of the therapy by sharing data with healthcare professionals. It is required when life-threatening events can still occur after hospital discharge such as neonatal apnea. However, multiple sources of external noise could affect data quality and/or increase the misdetection rate. In this study, we developed a mechatronic platform for sensor characterizations and a framework to manage data in the context of neonatal apnea. The platform can simulate the movement of the abdomen in different plausible newborn positions by merging data acquired simultaneously from three-axis accelerometers and infrared sensors. We simulated nine apnea conditions combining three different linear displacements and body postures in the presence of self-generated external noise, showing how it is possible to reduce errors near to zero in phenomena detection. Finally, the development of a smart 8Ws-based software and a customizable mobile application were proposed to facilitate data management and interpretation, classifying the alerts to guarantee the correct information sharing without specialized skills.
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spelling pubmed-87497242022-01-12 Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation Foresti, Ruben Statello, Rosario Delmonte, Nicola Lo Muzio, Francesco Paolo Rozzi, Giacomo Miragoli, Michele Sarli, Leopoldo Ferrari, Gianluigi Macaluso, Claudio Maggio, Marcello Giuseppe Pisani, Francesco Costantino, Cosimo Sensors (Basel) Communication Home monitoring supports the continuous improvement of the therapy by sharing data with healthcare professionals. It is required when life-threatening events can still occur after hospital discharge such as neonatal apnea. However, multiple sources of external noise could affect data quality and/or increase the misdetection rate. In this study, we developed a mechatronic platform for sensor characterizations and a framework to manage data in the context of neonatal apnea. The platform can simulate the movement of the abdomen in different plausible newborn positions by merging data acquired simultaneously from three-axis accelerometers and infrared sensors. We simulated nine apnea conditions combining three different linear displacements and body postures in the presence of self-generated external noise, showing how it is possible to reduce errors near to zero in phenomena detection. Finally, the development of a smart 8Ws-based software and a customizable mobile application were proposed to facilitate data management and interpretation, classifying the alerts to guarantee the correct information sharing without specialized skills. MDPI 2021-12-30 /pmc/articles/PMC8749724/ /pubmed/35009792 http://dx.doi.org/10.3390/s22010249 Text en © 2021 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 Communication
Foresti, Ruben
Statello, Rosario
Delmonte, Nicola
Lo Muzio, Francesco Paolo
Rozzi, Giacomo
Miragoli, Michele
Sarli, Leopoldo
Ferrari, Gianluigi
Macaluso, Claudio
Maggio, Marcello Giuseppe
Pisani, Francesco
Costantino, Cosimo
Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation
title Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation
title_full Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation
title_fullStr Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation
title_full_unstemmed Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation
title_short Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation
title_sort bionic for training: smart framework design for multisensor mechatronic platform validation
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749724/
https://www.ncbi.nlm.nih.gov/pubmed/35009792
http://dx.doi.org/10.3390/s22010249
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