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An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions
PURPOSE: Breathing parameters change with activity and posture, but currently available solutions can perform measurements only during static conditions. METHODS: This article presents an innovative wearable sensor system constituted by three inertial measurement units to simultaneously estimate res...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9970135/ https://www.ncbi.nlm.nih.gov/pubmed/36849621 http://dx.doi.org/10.1007/s13239-023-00657-3 |
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author | Angelucci, Alessandra Aliverti, Andrea |
author_facet | Angelucci, Alessandra Aliverti, Andrea |
author_sort | Angelucci, Alessandra |
collection | PubMed |
description | PURPOSE: Breathing parameters change with activity and posture, but currently available solutions can perform measurements only during static conditions. METHODS: This article presents an innovative wearable sensor system constituted by three inertial measurement units to simultaneously estimate respiratory rate (RR) in static and dynamic conditions and perform human activity recognition (HAR) with the same sensing principle. Two units are aimed at detecting chest wall breathing-related movements (one on the thorax, one on the abdomen); the third is on the lower back. All units compute the quaternions describing the subject’s movement and send data continuously with the ANT transmission protocol to an app. The 20 healthy subjects involved in the research (9 men, 11 women) were between 23 and 54 years old, with mean age 26.8, mean height 172.5 cm and mean weight 66.9 kg. Data from these subjects during different postures or activities were collected and analyzed to extract RR. RESULTS: Statistically significant differences between dynamic activities (“walking slow”, “walking fast”, “running” and “cycling”) and static postures were detected (p < 0.05), confirming the obtained measurements are in line with physiology even during dynamic activities. Data from the reference unit only and from all three units were used as inputs to artificial intelligence methods for HAR. When the data from the reference unit were used, the Gated Recurrent Unit was the best performing method (97% accuracy). With three units, a 1D Convolutional Neural Network was the best performing (99% accuracy). CONCLUSION: Overall, the proposed solution shows it is possible to perform simultaneous HAR and RR measurements in static and dynamic conditions with the same sensor system. |
format | Online Article Text |
id | pubmed-9970135 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-99701352023-02-28 An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions Angelucci, Alessandra Aliverti, Andrea Cardiovasc Eng Technol Original Article PURPOSE: Breathing parameters change with activity and posture, but currently available solutions can perform measurements only during static conditions. METHODS: This article presents an innovative wearable sensor system constituted by three inertial measurement units to simultaneously estimate respiratory rate (RR) in static and dynamic conditions and perform human activity recognition (HAR) with the same sensing principle. Two units are aimed at detecting chest wall breathing-related movements (one on the thorax, one on the abdomen); the third is on the lower back. All units compute the quaternions describing the subject’s movement and send data continuously with the ANT transmission protocol to an app. The 20 healthy subjects involved in the research (9 men, 11 women) were between 23 and 54 years old, with mean age 26.8, mean height 172.5 cm and mean weight 66.9 kg. Data from these subjects during different postures or activities were collected and analyzed to extract RR. RESULTS: Statistically significant differences between dynamic activities (“walking slow”, “walking fast”, “running” and “cycling”) and static postures were detected (p < 0.05), confirming the obtained measurements are in line with physiology even during dynamic activities. Data from the reference unit only and from all three units were used as inputs to artificial intelligence methods for HAR. When the data from the reference unit were used, the Gated Recurrent Unit was the best performing method (97% accuracy). With three units, a 1D Convolutional Neural Network was the best performing (99% accuracy). CONCLUSION: Overall, the proposed solution shows it is possible to perform simultaneous HAR and RR measurements in static and dynamic conditions with the same sensor system. Springer International Publishing 2023-02-27 2023 /pmc/articles/PMC9970135/ /pubmed/36849621 http://dx.doi.org/10.1007/s13239-023-00657-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Angelucci, Alessandra Aliverti, Andrea An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions |
title | An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions |
title_full | An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions |
title_fullStr | An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions |
title_full_unstemmed | An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions |
title_short | An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions |
title_sort | imu-based wearable system for respiratory rate estimation in static and dynamic conditions |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9970135/ https://www.ncbi.nlm.nih.gov/pubmed/36849621 http://dx.doi.org/10.1007/s13239-023-00657-3 |
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