volume | PIER Journals

Abstract

Contactless identifying different life states can result in improved rescue strategies in post-disaster rescues (such as earthquake and mine accident). If the buried targets are identified extremely endangered with very poor life states, the rescuing principles should be time first. Conversely, if the life states of the buried targets are relatively good, more reliable and safer methods should be given priority although they may cost a little more time. Unfortunately, there are few corresponding reports in life states identification, and current researches mainly focus on detecting or locating under penetration condition. This paper conducts a research on the change laws of physiological parameters of six New Zealand white rabbits, 3 females and 3 males. Experimental condition is under water and food deprivation to simulate one of the typical trapped situations of buried targets in post-disaster rescuing missions. Respiration is synchronously detected by an ultra-wideband (UWB) system in non-contact and an RM6240E system in contact. Heart rate, weight, and anal temperature are measured in contact measurement meanwhile. Over the time under water and food deprivation condition, there are typical and regular varieties in the respiration waveforms and heart rate values, which provide the possibility to identify different life states. Particularly, the respiration waveform changes in UWB radar signals are envisioned to be applied in practical post-disaster rescue where only UWB radar can penetrate ruins through penetrating measurement method.

1. Li, C. and J. Lin, Microwave Noncontact Motion Sensing and Analysis, John Wiley & Sons Press, 2014.

2. Zhang, Y., F. Qi, H. Lv, F. Liang, and J. Wang, "Bioradar technology: Recent research and advancements," IEEE Microwave Magazine, Vol. 20, 58-73, 2019.
doi:10.1109/MMM.2019.2915491 Google Scholar

3. Hong, H., L. Zhang, H. Zhao, H. Chu, C. Gu, M. Brown, X. Zhu, and C. Li, "Microwave sensing and sleep: Noncontact sleep-monitoring technology with microwave biomedical radar," IEEE Microwave Magazine, Vol. 20, No. 8, 18-29, 2019.
doi:10.1109/MMM.2019.2915469 Google Scholar

4. Qi, F., H. Lv, J. Wang, and A. Fathy, "Quantitative evaluation of channel micro-Doppler capacity for MIMO UWB radar human activity signals based on time-frequency signatures," IEEE Transactions on Geoscience and Remote Sensing, 1-14, 2020. Google Scholar

5. Ren, N., X. Quan, and S. H. Cho, "Algorithm for gesture recognition using an IR-UWB radar sensor," Journal of Computational Chemistry, Vol. 4, No. 3, 95-100, 2016. Google Scholar

6. Ren, L., T. Nghia, F. Foroughian, K. Naishadham, J. Piou, O. Kilic, and A. Fathy, "Short-time state-space method for micro-doppler identification of walking subject using UWB impulse doppler radar," IEEE Transactions on Microwave Theory and Techniques, Vol. 66, No. 7, 3521-3534, 2018.
doi:10.1109/TMTT.2018.2829523 Google Scholar

7. Kidera, S., T. Sakamoto, and T. Sato, "Accurate UWB radar three-dimensional imaging algorithm for a complex boundary without range point connections," IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 4, 1993-2004, 2010.
doi:10.1109/TGRS.2009.2036909 Google Scholar

8. Narayanan, R. M., "Through-wall radar imaging using UWB noise waveforms," Journal of the Franklin Institute, Vol. 345, No. 6, 659-678, 2008.
doi:10.1016/j.jfranklin.2008.03.004 Google Scholar

9. Donelli, M., "A rescue radar system for the detection of victims trapped under rubble based on the independent component analysis algorithm," Progress In Electromagnetics Research Letters, Vol. 19, 173-181, 2011.
doi:10.2528/PIERM11061206 Google Scholar

10. Donelli, M. and F. Viani, "Life signals detection system based on a continuous-wave X-band radar," Electronics Letters, Vol. 52, No. 23, 1903-1904, 2016.
doi:10.1049/el.2016.2902 Google Scholar

11. Bao, R., Z. Yang, Y. Cheng, and H. Liu, "Short-range moving human detection based-on cascaded spatial-temporal three-stages detector in UWB radar," International Conference on Signal Processing, Copenhagen, Denmark, April 2019. Google Scholar

12. Ma, Y., F. Liang, P. Wang, H. Lv, X. Yu, Y. Zhang, and J. Wang, "An accurate method to distinguish between stationary human and dog targets under through-wall condition using UWB radar," Remote Sensing, Vol. 11, No. 21, 2571, 2019.
doi:10.3390/rs11212571 Google Scholar

13. Ma, Y., F. Qi, P. Wang, F. Liang, H. Lv, X. Yu, Z. Li, H. Xue, J. Wang, and Y. Zhang, "Multiscale residual attention network for distinguishing stationary humans and common animals under through-wall condition using ultra-wideband radar," IEEE Access, 99, 2020. Google Scholar

14. Phinikaridou, A., K. Hallock, Y. Qiao, and J. Hamilton, "A robust rabbit model of human atherosclerosis and atherothrombosis," J. Lipid Res., Vol. 50, No. 5, 787-797, 2009.
doi:10.1194/jlr.M800460-JLR200 Google Scholar

15. Wang, Y., "Study on the technology of distinguishing between humans and animals via UWB bio-radar,", Ph.D. Thesis, The Fourth Military Medical University, Xi'an, Shaanxi, China, 2014. Google Scholar

16. Li, Z., W. Li, H. Lv, Y. Zhang, X. Jing, and J. Wang, "A novel method for respiration-like clutter cancellation in life detection by dual-frequency IR-UWB radar," IEEE Transactions on Microwave Theory and Techniques, Vol. 61, 2086-2092, 2013.
doi:10.1109/TMTT.2013.2247054 Google Scholar

Dr. Zhao Li

Dr. Yangyang Ma

Dr. Fu Gui Qi

Dr. Fulai Liang

Dr. Xiao Yu

Dr. Yang Zhang

Prof. Jianqi Wang

Dr. Guohua Lu