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Non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions
BACKGROUND: Currently, many imaging photoplethysmography (IPPG) researches have reported non-contact measurements of physiological parameters, such as heart rate (HR), respiratory rate (RR), etc. However, it is accepted that only HR measurement has been mature for applications, and other estimations...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5439118/ https://www.ncbi.nlm.nih.gov/pubmed/28249595 http://dx.doi.org/10.1186/s12938-016-0300-0 |
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author | Wei, Bing He, Xuan Zhang, Chao Wu, Xiaopei |
author_facet | Wei, Bing He, Xuan Zhang, Chao Wu, Xiaopei |
author_sort | Wei, Bing |
collection | PubMed |
description | BACKGROUND: Currently, many imaging photoplethysmography (IPPG) researches have reported non-contact measurements of physiological parameters, such as heart rate (HR), respiratory rate (RR), etc. However, it is accepted that only HR measurement has been mature for applications, and other estimations are relatively incapable for reliable applications. Thus, it is worth keeping on persistent studies. Besides, there are some issues commonly involved in these approaches need to be explored further. For example, motion artifact attenuation, an intractable problem, which is being attempted to be resolved by sophisticated video tracking and detection algorithms. METHODS: This paper proposed a blind source separation-based method that could synchronously measure RR and HR in non-contact way. A dual region of interest on facial video image was selected to yield 6-channels Red/Green/Blue signals. By applying Second-Order Blind Identification algorithm to those signals generated above, we obtained 6-channels outputs that contain blood volume pulse (BVP) and respiratory motion artifact. We defined this motion artifact as respiratory signal (RS). For the automatic selections of the RS and BVP among these outputs, we devised a kurtosis-based identification strategy, which guarantees the dynamic RR and HR monitoring available. RESULTS: The experimental results indicated that, the estimation by the proposed method has an impressive performance compared with the measurement of the commercial medical sensors. CONCLUSIONS: The proposed method achieved dynamic measurement of RR and HR, and the extension and revision of it may have the potentials for more physiological signs detection, such as heart rate variability, eye blinking, nose wrinkling, yawn, as well as other muscular movements. Thus, it might provide a promising approach for IPPG-based applications such as emotion computation and fatigue detection, etc. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12938-016-0300-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5439118 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54391182017-05-23 Non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions Wei, Bing He, Xuan Zhang, Chao Wu, Xiaopei Biomed Eng Online Research BACKGROUND: Currently, many imaging photoplethysmography (IPPG) researches have reported non-contact measurements of physiological parameters, such as heart rate (HR), respiratory rate (RR), etc. However, it is accepted that only HR measurement has been mature for applications, and other estimations are relatively incapable for reliable applications. Thus, it is worth keeping on persistent studies. Besides, there are some issues commonly involved in these approaches need to be explored further. For example, motion artifact attenuation, an intractable problem, which is being attempted to be resolved by sophisticated video tracking and detection algorithms. METHODS: This paper proposed a blind source separation-based method that could synchronously measure RR and HR in non-contact way. A dual region of interest on facial video image was selected to yield 6-channels Red/Green/Blue signals. By applying Second-Order Blind Identification algorithm to those signals generated above, we obtained 6-channels outputs that contain blood volume pulse (BVP) and respiratory motion artifact. We defined this motion artifact as respiratory signal (RS). For the automatic selections of the RS and BVP among these outputs, we devised a kurtosis-based identification strategy, which guarantees the dynamic RR and HR monitoring available. RESULTS: The experimental results indicated that, the estimation by the proposed method has an impressive performance compared with the measurement of the commercial medical sensors. CONCLUSIONS: The proposed method achieved dynamic measurement of RR and HR, and the extension and revision of it may have the potentials for more physiological signs detection, such as heart rate variability, eye blinking, nose wrinkling, yawn, as well as other muscular movements. Thus, it might provide a promising approach for IPPG-based applications such as emotion computation and fatigue detection, etc. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12938-016-0300-0) contains supplementary material, which is available to authorized users. BioMed Central 2017-01-17 /pmc/articles/PMC5439118/ /pubmed/28249595 http://dx.doi.org/10.1186/s12938-016-0300-0 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Wei, Bing He, Xuan Zhang, Chao Wu, Xiaopei Non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions |
title | Non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions |
title_full | Non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions |
title_fullStr | Non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions |
title_full_unstemmed | Non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions |
title_short | Non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions |
title_sort | non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5439118/ https://www.ncbi.nlm.nih.gov/pubmed/28249595 http://dx.doi.org/10.1186/s12938-016-0300-0 |
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