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Posture-Specific Breathing Detection

Human respiratory activity parameters are important indicators of vital signs. Most respiratory activity detection methods are naïve abd simple and use invasive detection technology. Non-invasive breathing detection methods are the solution to these limitations. In this research, we propose a non-in...

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Detalles Bibliográficos
Autores principales: Guan, Hualin, Yang, Xiaodong, Sun, Wanrong, Ren, Aifeng, Fan, Dou, Zhao, Nan, Guan, Lei, Haider, Daniyal, Abbasi, Qammer H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308544/
https://www.ncbi.nlm.nih.gov/pubmed/30558319
http://dx.doi.org/10.3390/s18124443
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author Guan, Hualin
Yang, Xiaodong
Sun, Wanrong
Ren, Aifeng
Fan, Dou
Zhao, Nan
Guan, Lei
Haider, Daniyal
Abbasi, Qammer H.
author_facet Guan, Hualin
Yang, Xiaodong
Sun, Wanrong
Ren, Aifeng
Fan, Dou
Zhao, Nan
Guan, Lei
Haider, Daniyal
Abbasi, Qammer H.
author_sort Guan, Hualin
collection PubMed
description Human respiratory activity parameters are important indicators of vital signs. Most respiratory activity detection methods are naïve abd simple and use invasive detection technology. Non-invasive breathing detection methods are the solution to these limitations. In this research, we propose a non-invasive breathing activity detection method based on C-band sensing. Traditional non-invasive detection methods require special hardware facilities that cannot be used in ordinary environments. Based on this, a multi-input, multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system based on 802.11n protocol is proposed in this paper. Our system improves the traditional data processing method and has stronger robustness and lower bit relative error. The system detects the respiratory activity of different body postures, captures and analyses the information, and determines the influence of different body postures on human respiratory activity.
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spelling pubmed-63085442019-01-04 Posture-Specific Breathing Detection Guan, Hualin Yang, Xiaodong Sun, Wanrong Ren, Aifeng Fan, Dou Zhao, Nan Guan, Lei Haider, Daniyal Abbasi, Qammer H. Sensors (Basel) Article Human respiratory activity parameters are important indicators of vital signs. Most respiratory activity detection methods are naïve abd simple and use invasive detection technology. Non-invasive breathing detection methods are the solution to these limitations. In this research, we propose a non-invasive breathing activity detection method based on C-band sensing. Traditional non-invasive detection methods require special hardware facilities that cannot be used in ordinary environments. Based on this, a multi-input, multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system based on 802.11n protocol is proposed in this paper. Our system improves the traditional data processing method and has stronger robustness and lower bit relative error. The system detects the respiratory activity of different body postures, captures and analyses the information, and determines the influence of different body postures on human respiratory activity. MDPI 2018-12-15 /pmc/articles/PMC6308544/ /pubmed/30558319 http://dx.doi.org/10.3390/s18124443 Text en © 2018 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Guan, Hualin
Yang, Xiaodong
Sun, Wanrong
Ren, Aifeng
Fan, Dou
Zhao, Nan
Guan, Lei
Haider, Daniyal
Abbasi, Qammer H.
Posture-Specific Breathing Detection
title Posture-Specific Breathing Detection
title_full Posture-Specific Breathing Detection
title_fullStr Posture-Specific Breathing Detection
title_full_unstemmed Posture-Specific Breathing Detection
title_short Posture-Specific Breathing Detection
title_sort posture-specific breathing detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308544/
https://www.ncbi.nlm.nih.gov/pubmed/30558319
http://dx.doi.org/10.3390/s18124443
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