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A Novel Non-Contact Detection and Identification Method for the Post-Disaster Compression State of Injured Individuals Using UWB Bio-Radar
Building collapse leads to mechanical injury, which is the main cause of injury and death, with crush syndrome as its most common complication. During the post-disaster search and rescue phase, if rescue personnel hastily remove heavy objects covering the bodies of injured individuals and fail to pr...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10451469/ https://www.ncbi.nlm.nih.gov/pubmed/37627790 http://dx.doi.org/10.3390/bioengineering10080905 |
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author | Shi, Ding Liang, Fulai Qiao, Jiahao Wang, Yaru Zhu, Yidan Lv, Hao Yu, Xiao Jiao, Teng Liao, Fuyuan Yan, Keding Wang, Jianqi Zhang, Yang |
author_facet | Shi, Ding Liang, Fulai Qiao, Jiahao Wang, Yaru Zhu, Yidan Lv, Hao Yu, Xiao Jiao, Teng Liao, Fuyuan Yan, Keding Wang, Jianqi Zhang, Yang |
author_sort | Shi, Ding |
collection | PubMed |
description | Building collapse leads to mechanical injury, which is the main cause of injury and death, with crush syndrome as its most common complication. During the post-disaster search and rescue phase, if rescue personnel hastily remove heavy objects covering the bodies of injured individuals and fail to provide targeted medical care, ischemia-reperfusion injury may be triggered, leading to rhabdomyolysis. This may result in disseminated intravascular coagulation or acute respiratory distress syndrome, further leading to multiple organ failure, which ultimately leads to shock and death. Using bio-radar to detect vital signs and identify compression states can effectively reduce casualties during the search for missing persons behind obstacles. A time-domain ultra-wideband (UWB) bio-radar was applied for the non-contact detection of human vital sign signals behind obstacles. An echo denoising algorithm based on PSO-VMD and permutation entropy was proposed to suppress environmental noise, along with a wounded compression state recognition network based on radar-life signals. Based on training and testing using over 3000 data sets from 10 subjects in different compression states, the proposed multiscale convolutional network achieved a 92.63% identification accuracy. This outperformed SVM and 1D-CNN models by 5.30% and 6.12%, respectively, improving the casualty rescue success and post-disaster precision. |
format | Online Article Text |
id | pubmed-10451469 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104514692023-08-26 A Novel Non-Contact Detection and Identification Method for the Post-Disaster Compression State of Injured Individuals Using UWB Bio-Radar Shi, Ding Liang, Fulai Qiao, Jiahao Wang, Yaru Zhu, Yidan Lv, Hao Yu, Xiao Jiao, Teng Liao, Fuyuan Yan, Keding Wang, Jianqi Zhang, Yang Bioengineering (Basel) Article Building collapse leads to mechanical injury, which is the main cause of injury and death, with crush syndrome as its most common complication. During the post-disaster search and rescue phase, if rescue personnel hastily remove heavy objects covering the bodies of injured individuals and fail to provide targeted medical care, ischemia-reperfusion injury may be triggered, leading to rhabdomyolysis. This may result in disseminated intravascular coagulation or acute respiratory distress syndrome, further leading to multiple organ failure, which ultimately leads to shock and death. Using bio-radar to detect vital signs and identify compression states can effectively reduce casualties during the search for missing persons behind obstacles. A time-domain ultra-wideband (UWB) bio-radar was applied for the non-contact detection of human vital sign signals behind obstacles. An echo denoising algorithm based on PSO-VMD and permutation entropy was proposed to suppress environmental noise, along with a wounded compression state recognition network based on radar-life signals. Based on training and testing using over 3000 data sets from 10 subjects in different compression states, the proposed multiscale convolutional network achieved a 92.63% identification accuracy. This outperformed SVM and 1D-CNN models by 5.30% and 6.12%, respectively, improving the casualty rescue success and post-disaster precision. MDPI 2023-07-30 /pmc/articles/PMC10451469/ /pubmed/37627790 http://dx.doi.org/10.3390/bioengineering10080905 Text en © 2023 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 Shi, Ding Liang, Fulai Qiao, Jiahao Wang, Yaru Zhu, Yidan Lv, Hao Yu, Xiao Jiao, Teng Liao, Fuyuan Yan, Keding Wang, Jianqi Zhang, Yang A Novel Non-Contact Detection and Identification Method for the Post-Disaster Compression State of Injured Individuals Using UWB Bio-Radar |
title | A Novel Non-Contact Detection and Identification Method for the Post-Disaster Compression State of Injured Individuals Using UWB Bio-Radar |
title_full | A Novel Non-Contact Detection and Identification Method for the Post-Disaster Compression State of Injured Individuals Using UWB Bio-Radar |
title_fullStr | A Novel Non-Contact Detection and Identification Method for the Post-Disaster Compression State of Injured Individuals Using UWB Bio-Radar |
title_full_unstemmed | A Novel Non-Contact Detection and Identification Method for the Post-Disaster Compression State of Injured Individuals Using UWB Bio-Radar |
title_short | A Novel Non-Contact Detection and Identification Method for the Post-Disaster Compression State of Injured Individuals Using UWB Bio-Radar |
title_sort | novel non-contact detection and identification method for the post-disaster compression state of injured individuals using uwb bio-radar |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10451469/ https://www.ncbi.nlm.nih.gov/pubmed/37627790 http://dx.doi.org/10.3390/bioengineering10080905 |
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