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Assessing Respiratory Activity by Using IMUs: Modeling and Validation

This study aimed to explore novel inertial measurement unit (IMU)-based strategies to estimate respiratory parameters in healthy adults lying on a bed while breathing normally. During the experimental sessions, the kinematics of the chest wall were contemporaneously collected through both a network...

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Autores principales: Monaco, Vito, Giustinoni, Carolina, Ciapetti, Tommaso, Maselli, Alessandro, Stefanini, Cesare
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950860/
https://www.ncbi.nlm.nih.gov/pubmed/35336355
http://dx.doi.org/10.3390/s22062185
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author Monaco, Vito
Giustinoni, Carolina
Ciapetti, Tommaso
Maselli, Alessandro
Stefanini, Cesare
author_facet Monaco, Vito
Giustinoni, Carolina
Ciapetti, Tommaso
Maselli, Alessandro
Stefanini, Cesare
author_sort Monaco, Vito
collection PubMed
description This study aimed to explore novel inertial measurement unit (IMU)-based strategies to estimate respiratory parameters in healthy adults lying on a bed while breathing normally. During the experimental sessions, the kinematics of the chest wall were contemporaneously collected through both a network of 9 IMUs and a set of 45 uniformly distributed reflective markers. All inertial kinematics were analyzed to identify a minimum set of signals and IMUs whose linear combination best matched the tidal volume measured by optoelectronic plethysmography. The resulting models were finally tuned and validated through a leave-one-out cross-validation approach to assess the extent to which they could accurately estimate a set of respiratory parameters related to three trunk compartments. The adopted methodological approach allowed us to identify two different models. The first, referred to as Model 1, relies on the 3D acceleration measured by three IMUs located on the abdominal compartment and on the lower costal margin. The second, referred to as Model 2, relies on only one component of the acceleration measured by two IMUs located on the abdominal compartment. Both models can accurately estimate the respiratory rate (relative error < 1.5%). Conversely, the duration of the respiratory phases and the tidal volume can be more accurately assessed by Model 2 (relative error < 5%) and Model 1 (relative error < 5%), respectively. We further discuss possible approaches to overcome limitations and improve the overall accuracy of the proposed approach.
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spelling pubmed-89508602022-03-26 Assessing Respiratory Activity by Using IMUs: Modeling and Validation Monaco, Vito Giustinoni, Carolina Ciapetti, Tommaso Maselli, Alessandro Stefanini, Cesare Sensors (Basel) Article This study aimed to explore novel inertial measurement unit (IMU)-based strategies to estimate respiratory parameters in healthy adults lying on a bed while breathing normally. During the experimental sessions, the kinematics of the chest wall were contemporaneously collected through both a network of 9 IMUs and a set of 45 uniformly distributed reflective markers. All inertial kinematics were analyzed to identify a minimum set of signals and IMUs whose linear combination best matched the tidal volume measured by optoelectronic plethysmography. The resulting models were finally tuned and validated through a leave-one-out cross-validation approach to assess the extent to which they could accurately estimate a set of respiratory parameters related to three trunk compartments. The adopted methodological approach allowed us to identify two different models. The first, referred to as Model 1, relies on the 3D acceleration measured by three IMUs located on the abdominal compartment and on the lower costal margin. The second, referred to as Model 2, relies on only one component of the acceleration measured by two IMUs located on the abdominal compartment. Both models can accurately estimate the respiratory rate (relative error < 1.5%). Conversely, the duration of the respiratory phases and the tidal volume can be more accurately assessed by Model 2 (relative error < 5%) and Model 1 (relative error < 5%), respectively. We further discuss possible approaches to overcome limitations and improve the overall accuracy of the proposed approach. MDPI 2022-03-11 /pmc/articles/PMC8950860/ /pubmed/35336355 http://dx.doi.org/10.3390/s22062185 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
Monaco, Vito
Giustinoni, Carolina
Ciapetti, Tommaso
Maselli, Alessandro
Stefanini, Cesare
Assessing Respiratory Activity by Using IMUs: Modeling and Validation
title Assessing Respiratory Activity by Using IMUs: Modeling and Validation
title_full Assessing Respiratory Activity by Using IMUs: Modeling and Validation
title_fullStr Assessing Respiratory Activity by Using IMUs: Modeling and Validation
title_full_unstemmed Assessing Respiratory Activity by Using IMUs: Modeling and Validation
title_short Assessing Respiratory Activity by Using IMUs: Modeling and Validation
title_sort assessing respiratory activity by using imus: modeling and validation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950860/
https://www.ncbi.nlm.nih.gov/pubmed/35336355
http://dx.doi.org/10.3390/s22062185
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