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YANG Xuan, WANG Ziying, ZHANG Li, et al. Noncontact multiperson respiratory detection method based on blind source separation[J]. Journal of Radars, in press. doi: 10.12000/JR24115
Citation: YANG Xuan, WANG Ziying, ZHANG Li, et al. Noncontact multiperson respiratory detection method based on blind source separation[J]. Journal of Radars, in press. doi: 10.12000/JR24115
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Noncontact Multiperson Respiratory Detection Method Based on Blind Source Separation

DOI: 10.12000/JR24115
  • 1.

    Nanjing University of Science and Technology, Nanjing 210000, China

  • 2.

    Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China

Funds:  The National Natural Science Foundation of China (62431013, 62301255, 62201259), Natural Science Foundation of Jiangsu Province (BK20220942, BK20220940), Fundamental Research Funds for the Central Universities (30923011026, 30923011006)
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    Abstract
  • In recent years, there has been an increasing interest in respiratory monitoring in multiperson environments and simultaneous monitoring of the health status of multiple people. Among the algorithms developed for multiperson respiratory detection, blind source separation algorithms have attracted the attention of researchers because they do not require prior information and are less dependent on hardware performance. However, in the context of multiperson respiratory monitoring, the current blind source separation algorithm usually separates phase signals as the source signal. This article compares the distance dimension and phase signals under Frequency-modulated continuous-wave radar, calculates the approximate error associated with using the phase signal as the source signal, and verifies the separation effect through simulations. The distance dimension signal is better to use as the source signal. In addition, this article proposes a multiperson respiratory signal separation algorithm based on noncircular complex independent component analysis and analyzes the impact of different respiratory signal parameters on the separation effect. Simulation and experimental measurements show that the proposed method is suitable for detecting multiperson respiratory signals under controlled conditions and can accurately separate respiratory signals when the angle of the two targets to the radar is 9.46°.

     

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    • References(37)
  • [1]
    CROSSLEY G H, BOYLE A, VITENSE H, et al. The CONNECT (Clinical Evaluation of Remote Notification to Reduce Time to Clinical Decision) trial: The value of wireless remote monitoring with automatic clinician alerts[J]. Journal of the American College of Cardiology, 2011, 57(10): 1181–1189. doi: 10.1016/j.jacc.2010.12.012.
    [2]
    NANGALIA V, PRYTHERCH D R, and SMITH G B. Health technology assessment review: Remote monitoring of vital signs-current status and future challenges[J]. Critical Care, 2010, 14(5): 233. doi: 10.1186/cc9208.
    [3]
    FIORANELLI F, LE KERNEC J, and SHAH S A. Radar for health care: Recognizing human activities and monitoring vital signs[J]. IEEE Potentials, 2019, 38(4): 16–23. doi: 10.1109/MPOT.2019.2906977.
    [4]
    DONG Shuqin, WEN Li, YE Yangtao, et al. A review on recent advancements of biomedical radar for clinical applications[J]. IEEE Open Journal of Engineering in Medicine and Biology, 2024, 5: 707–724. doi: 10.1109/OJEMB.2024.3401105.
    [5]
    ISHRAK M S, CAI Fulin, ISLAM S M M, et al. Doppler radar remote sensing of respiratory function[J]. Frontiers in Physiology, 2023, 14: 1130478. doi: 10.3389/fphys.2023.1130478.
    [6]
    ANTOLINOS E, GARCÍA-RIAL F, HERNÁNDEZ C, et al. Cardiopulmonary activity monitoring using millimeter wave radars[J]. Remote Sensing, 2020, 12(14): 2265. doi: 10.3390/rs12142265.
    [7]
    ADIB F, MAO Hongzi, KABELAC Z, et al. Smart homes that monitor breathing and heart rate[C]. The 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Republic of Korea, 2015: 837–846. doi: 10.1145/2702123.2702200.
    [8]
    ZHAO Heng, HONG Hong, MIAO Dongyu, et al. A noncontact breathing disorder recognition system using 2.4-GHz digital-IF Doppler radar[J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23(1): 208–217. doi: 10.1109/JBHI.2018.2817258.
    [9]
    PARK B K, BORIC-LUBECKE O, and LUBECKE V M. Arctangent demodulation with DC offset compensation in quadrature Doppler radar receiver systems[J]. IEEE Transactions on Microwave Theory and Techniques, 2007, 55(5): 1073–1079. doi: 10.1109/TMTT.2007.895653.
    [10]
    XIAO Yanming, LIN J, BORIC-LUBECKE O, et al. A Ka-band low power Doppler radar system for remote detection of cardiopulmonary motion[C]. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, 2006: 7151–7154. doi: 10.1109/IEMBS.2005.1616156.
    [11]
    LIN Jenshan, LUBECKE V M, BORIC-LUBECKE O, et al. A review on recent advances in Doppler radar sensors for noncontact healthcare monitoring[J]. IEEE Transactions on Microwave Theory and Techniques, 2013, 61(5): 2046–2060. doi: 10.1109/TMTT.2013.2256924.
    [12]
    PATERNIANI G, SGRECCIA D, DAVOLI A, et al. Radar-based monitoring of vital signs: A tutorial overview[J]. Proceedings of the IEEE, 2023, 111(3): 277–317. doi: 10.1109/JPROC.2023.3244362.
    [13]
    BORIC-LUBEKE O and LUBECKE V M. Wireless house calls: Using communications technology for health care and monitoring[J]. IEEE Microwave Magazine, 2002, 3(3): 43–48. doi: 10.1109/MMW.2002.1028361.
    [14]
    DONG Shuqin, WEN Li, LI Yuchen, et al. Remote respiratory variables tracking with biomedical radar-based IoT system during sleep[J]. IEEE Internet of Things Journal, 2024, 11(11): 19937–19948. doi: 10.1109/JIOT.2024.3367932.
    [15]
    GUAN Lei, WU Tong, YANG Xiaodong, et al. Multiperson respiratory monitoring using single-channel continuous-wave radar with time modulated array[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 4008111. doi: 10.1109/TIM.2023.3287258.
    [16]
    WANG Shuxuan, HAN Chong, GUO Jian, et al. MM-FGRM: Fine-grained respiratory monitoring using MIMO millimeter wave radar[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 4000913. doi: 10.1109/TIM.2023.3334353.
    [17]
    PHAN T, KILIC O, NAHAR S, et al. Accuracy investigation of SFCW radar in human vital signs detection for subject's relative position[C]. 2017 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, San Diego, USA, 2017: 577–578. doi: 10.1109/APUSNCURSINRSM.2017.8072331.
    [18]
    MERCURI M, LIU Yaohong, SHEELAVANT S, et al. Digital linear discrete FMCW radar for healthcare applications[C]. 2019 IEEE MTT-S International Microwave Symposium (IMS), Boston, USA, 2019: 144–147. doi: 10.1109/MWSYM.2019.8701012.
    [19]
    YAN Jiaming, HONG Hong, ZHAO Heng, et al. Through-wall multiple targets vital signs tracking based on VMD algorithm[J]. Sensors, 2016, 16(8): 1293. doi: 10.3390/s16081293.
    [20]
    EDER Y and ELDAR Y C. Sparsity-based multi-person non-contact vital signs monitoring via FMCW radar[J]. IEEE Journal of Biomedical and Health Informatics, 2023, 27(6): 2806–2817. doi: 10.1109/JBHI.2023.3255740.
    [21]
    XIONG Junjun, HONG Hong, ZHANG Hongqiang, et al. Multitarget respiration detection with adaptive digital beamforming technique based on SIMO radar[J]. IEEE Transactions on Microwave Theory and Techniques, 2020, 68(11): 4814–4824. doi: 10.1109/TMTT.2020.3020082.
    [22]
    KODA T, SAKAMOTO T, OKUMURA S, et al. Noncontact respiratory measurement for multiple people at arbitrary locations using array radar and respiratory-space clustering[J]. IEEE Access, 2021, 9: 106895–106906. doi: 10.1109/ACCESS.2021.3099821.
    [23]
    SAKAMOTO T and YAMASHITA K. Noncontact measurement of autonomic nervous system activities based on heart rate variability using ultra-wideband array radar[J]. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 2020, 4(3): 208–215. doi: 10.1109/JERM.2019.2948827.
    [24]
    ISLAM S M M, YAVARI E, RAHMAN A, et al. Separation of respiratory signatures for multiple subjects using independent component analysis with the JADE algorithm[C]. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, USA, 2018: 1234–1237. doi: 10.1109/EMBC.2018.8512583.
    [25]
    ISLAM S M M, BORIC-LUBECKE O, and LUBEKCE V M. Concurrent respiration monitoring of multiple subjects by phase-comparison monopulse radar using independent component analysis (ICA) with JADE algorithm and direction of arrival (DOA)[J]. IEEE Access, 2020, 8: 73558–73569. doi: 10.1109/ACCESS.2020.2988038.
    [26]
    ZHANG Li, LIU Yuan, HONG Hong, et al. Noncontact multi-target respiration sensing using SIMO radar with UBSS method[J]. IEEE Microwave and Wireless Components Letters, 2022, 32(3): 210–213. doi: 10.1109/LMWC.2021.3138767.
    [27]
    LIU Jingtao, LI Yuchen, LI Changzhi, et al. Accurate measurement of human vital signs with linear FMCW radars under proximity stationary clutters[J]. IEEE Transactions on Biomedical Circuits and Systems, 2021, 15(6): 1393–1404. doi: 10.1109/TBCAS.2021.3123830.
    [28]
    ZAKRZEWSKI M, RAITTINEN H, and VANHALA J. Comparison of center estimation algorithms for heart and respiration monitoring with microwave Doppler radar[J]. IEEE Sensors Journal, 2012, 12(3): 627–634. doi: 10.1109/JSEN.2011.2119299.
    [29]
    PICINBONO B. On circularity[J]. IEEE Transactions on Signal Processing, 1994, 42(12): 3473–3482. doi: 10.1109/78.340781.
    [30]
    YUE Shichao, HE Hao, WANG Hao, et al. Extracting multi-person respiration from entangled RF signals[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2(2): 86. doi: 10.1145/3214289.
    [31]
    ERIKSSON J and KOIVUNEN V. Complex-valued ICA using second order statistics[C]. The 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004, Sao Luis, Brazil, 2004: 183–192. doi: 10.1109/MLSP.2004.1422973.
    [32]
    ERIKSSON J and KOIVUNEN V. Complex random vectors and ICA models: Identifiability, uniqueness, and separability[J]. IEEE Transactions on Information Theory, 2006, 52(3): 1017–1029. doi: 10.1109/TIT.2005.864440.
    [33]
    BRANDWOOD D H. A complex gradient operator and its application in adaptive array theory[J]. IEE Proceedings F (Communications, Radar and Signal Processing), 1983, 130(1): 11–16. doi: 10.1049/ip-f-1.1983.0003.
    [34]
    CARDOSO J F and SOULOUMIAC A. Blind beamforming for non-Gaussian signals[C]. IEE Proceedings F (Radar and Signal Processing), 1993, 140(6): 362–370. doi: 10.1049/ip-f-2.1993.0054.
    [35]
    NOVEY M and ADALI T. Complex fixed-point ICA algorithm for separation of QAM sources using Gaussian mixture model[C]. 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07, Honolulu, USA, 2007: II-445–II-448. doi: 10.1109/ICASSP.2007.366268.
    [36]
    NOVEY M and ADALI T. On extending the complex FastICA algorithm to noncircular sources[J]. IEEE Transactions on Signal Processing, 2008, 56(5): 2148–2154. doi: 10.1109/TSP.2007.911278.
    [37]
    RODRIGUEZ P A, CALHOUN V D, and ADALI T. De-noising, phase ambiguity correction and visualization techniques for complex-valued ICA of group fMRI data[J]. Pattern Recognition, 2012, 45(6): 2050–2063. doi: 10.1016/j.patcog.2011.04.033.
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