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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/8/905 |
| _version_ | 1827730329396314112 |
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| author | Ding Shi Fulai Liang Jiahao Qiao Yaru Wang Yidan Zhu Hao Lv Xiao Yu Teng Jiao Fuyuan Liao Keding Yan Jianqi Wang Yang Zhang |
| author_facet | Ding Shi Fulai Liang Jiahao Qiao Yaru Wang Yidan Zhu Hao Lv Xiao Yu Teng Jiao Fuyuan Liao Keding Yan Jianqi Wang Yang Zhang |
| author_sort | Ding Shi |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T00:07:41Z |
| format | Article |
| id | doaj.art-9d70de1721b64e77beda7f9a75132514 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-11T00:07:41Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-9d70de1721b64e77beda7f9a751325142023-11-19T00:17:40ZengMDPI AGBioengineering2306-53542023-07-0110890510.3390/bioengineering10080905A Novel Non-Contact Detection and Identification Method for the Post-Disaster Compression State of Injured Individuals Using UWB Bio-RadarDing Shi0Fulai Liang1Jiahao Qiao2Yaru Wang3Yidan Zhu4Hao Lv5Xiao Yu6Teng Jiao7Fuyuan Liao8Keding Yan9Jianqi Wang10Yang Zhang11Department of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, ChinaDepartment of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, ChinaDepartment of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, ChinaDepartment of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, ChinaDepartment of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, ChinaDepartment of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, ChinaDepartment of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, ChinaDepartment of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, ChinaDepartment of Biomedical Engineering, School of Electronic and Information Engineering, Xi’an Technological University, Xi’an 710032, ChinaDepartment of Biomedical Engineering, School of Electronic and Information Engineering, Xi’an Technological University, Xi’an 710032, ChinaDepartment of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, ChinaDepartment of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, ChinaBuilding 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.https://www.mdpi.com/2306-5354/10/8/905bio-radarcrushing injuryultra-widebandvariational modal decompositionconvolutional neural network |
| spellingShingle | Ding Shi Fulai Liang Jiahao Qiao Yaru Wang Yidan Zhu Hao Lv Xiao Yu Teng Jiao Fuyuan Liao Keding Yan Jianqi Wang Yang Zhang A Novel Non-Contact Detection and Identification Method for the Post-Disaster Compression State of Injured Individuals Using UWB Bio-Radar Bioengineering bio-radar crushing injury ultra-wideband variational modal decomposition convolutional neural network |
| 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 | bio-radar crushing injury ultra-wideband variational modal decomposition convolutional neural network |
| url | https://www.mdpi.com/2306-5354/10/8/905 |
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