Citation: | FANG Zhen, JIAN Pu, ZHANG Hao, et al. Review of noncontact medical and health monitoring technologies based on FMCW radar[J]. Journal of Radars, 2022, 11(3): 499–516. doi: 10.12000/JR22019 |
[1] |
LU Shan, WANG Anzhi, JING Shenqi, et al. A study on service-oriented smart medical systems combined with key algorithms in the IoT environment[J]. China Communications, 2019, 16(9): 235–249. doi: 10.23919/JCC.2019.09.018
|
[2] |
AL-MAHMUD O, KHAN K, ROY R, et al. Internet of things (IoT) based smart health care medical box for elderly people[C]. 2020 International Conference for Emerging Technology (INCET), Belgaum, India, 2020: 1–6.
|
[3] |
VILLENEUVE E, HARWIN W, HOLDERBAUM W, et al. Reconstruction of angular kinematics from wrist-worn inertial sensor data for smart home healthcare[J]. IEEE Access, 2017, 5: 2351–2363. doi: 10.1109/ACCESS.2016.2640559
|
[4] |
MAHFOUZ M R, KUHN M J, and TO G. Wireless medical devices: A review of current research and commercial systems[C]. 2013 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems, Austin, USA, 2013: 16–18.
|
[5] |
PARK E, KIM J H, NAM H S, et al. Requirement analysis and implementation of smart emergency medical services[J]. IEEE Access, 2018, 6: 42022–42029. doi: 10.1109/ACCESS.2018.2861711
|
[6] |
LIU Jian, CHEN Yingying, WANG Yan, et al. Monitoring vital signs and postures during sleep using WiFi signals[J]. IEEE Internet of Things Journal, 2018, 5(3): 2071–2084. doi: 10.1109/JIOT.2018.2822818
|
[7] |
WANG Hao, ZHANG Daqing, WANG Yasha, et al. RT-Fall: A real-time and contactless fall detection system with commodity WiFi devices[J]. IEEE Transactions on Mobile Computing, 2017, 16(2): 511–526. doi: 10.1109/TMC.2016.2557795
|
[8] |
WANG Xuyu, YANG Chao, and MAO Shiwen. PhaseBeat: Exploiting CSI phase data for vital sign monitoring with commodity WiFi devices[C]. 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, USA, 2017: 1230–1239.
|
[9] |
LIN Feng, SONG Chen, ZHUANG Yan, et al. Cardiac scan: A non-contact and continuous heart-based user authentication system[C]. The 23rd Annual International Conference on Mobile Computing and Networking, Snowbird, USA, 2017: 315–328.
|
[10] |
WILL C, SHI K, SCHELLENBERGER S, et al. Local pulse wave detection using continuous wave radar systems[J]. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 2017, 1(2): 81–89. doi: 10.1109/JERM.2017.2766567
|
[11] |
RAHMAN T, ADAMS A T, RAVICHANDRAN R V, et al. DoppleSleep: A contactless unobtrusive sleep sensing system using short-range doppler radar[C]. The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 2015: 39–50.
|
[12] |
YANG Yang, HOU Chunping, LANG Yue, et al. Open-set human activity recognition based on micro-Doppler signatures[J]. Pattern Recognition, 2019, 85: 60–69. doi: 10.1016/j.patcog.2018.07.030
|
[13] |
LARSON E C, GOEL M, BORIELLO G, et al. SpiroSmart: Using a microphone to measure lung function on a mobile phone[C]. The 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, USA, 2012: 280–289.
|
[14] |
ZHANG Fusang, WANG Zhi, JIN Beihong, et al. Your smart speaker can “hear” your heartbeat![J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020, 4(4): 161. doi: 10.1145/3432237
|
[15] |
陆佳鑫. 基于深度神经网络的人体跌倒碰撞前行为检测研究[D]. [硕士论文], 电子科技大学, 2021.
LU Jiaxin. A research of human pre-impact fall detection based on deep neural network[D]. [Master dissertation], University of Electronic Science and Technology of China, 2021.
|
[16] |
SADREAZAMI H, BOLIC M, and RAJAN S. Fall detection using standoff radar-based sensing and deep convolutional neural network[J]. IEEE Transactions on Circuits and Systems II:Express Briefs, 2020, 67(1): 197–201. doi: 10.1109/TCSII.2019.2904498
|
[17] |
PENG Zhengyu and LI Changzhi. Portable microwave radar systems for short-range localization and life tracking: A review[J]. Sensors, 2019, 19(5): 1136. doi: 10.3390/s19051136
|
[18] |
JARDAK S, ALOUINI M S, KIURU T, et al. Compact mmWave FMCW radar: Implementation and performance analysis[J]. IEEE Aerospace and Electronic Systems Magazine, 2019, 34(2): 36–44. doi: 10.1109/MAES.2019.180130
|
[19] |
PATOLE S M, TORLAK M, WANG Dan, et al. Automotive radars: A review of signal processing techniques[J]. IEEE Signal Processing Magazine, 2017, 34(2): 22–35. doi: 10.1109/MSP.2016.2628914
|
[20] |
LI Changzhi, PENG Zhengyu, HUANG T Y, et al. A review on recent progress of portable short-range noncontact microwave radar systems[J]. IEEE Transactions on Microwave Theory and Techniques, 2017, 65(5): 1692–1706. doi: 10.1109/TMTT.2017.2650911
|
[21] |
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.
|
[22] |
WANG Fengyu, ZHANG Feng, WU Chenshu, et al. ViMo: Multiperson vital sign monitoring using commodity millimeter-wave radio[J]. IEEE Internet of Things Journal, 2021, 8(3): 1294–1307. doi: 10.1109/JIOT.2020.3004046
|
[23] |
CHEN Baozhan, QIAO Siyuan, ZHAO Jie, et al. A security awareness and protection system for 5G smart healthcare based on zero-trust architecture[J]. IEEE Internet of Things Journal, 2021, 8(13): 10248–10263. doi: 10.1109/JIOT.2020.3041042
|
[24] |
李健. 24GHz调频连续波雷达信号处理技术研究[D]. [硕士论文], 南京理工大学, 2017.
LI Jian. Research on signal processing technology of 24GHz FMCW radar[D]. [Master dissertation], Nanjing University of Science and Technology, 2017.
|
[25] |
李艳莉. 毫米波通信技术的研究现状和进展[C]. 四川省通信学会2010年学术年会论文集, 成都, 2010: 46–49.
LI Yanli. Research status and progress of millimeter wave communication technology[C]. Papers of the 2010 Annual Conference of Sichuan Communications Society, Chengdu, China, 2010: 46–49.
|
[26] |
CHADWICK P E. Regulations and standards for wireless applications in eHealth[C]. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 2007: 6170–6173.
|
[27] |
YANG Xiaodong, FAN Dou, REN Aifeng, et al. Sleep apnea syndrome sensing at C-band[J]. IEEE Journal of Translational Engineering in Health and Medicine, 2018, 6: 2701008. doi: 10.1109/JTEHM.2018.2879085
|
[28] |
OHAYON M, WICKWIRE E M, HIRSHKOWITZ M, et al. National sleep foundation’s sleep quality recommendations: First report[J]. Sleep Health, 2017, 3(1): 6–19. doi: 10.1016/j.sleh.2016.11.006
|
[29] |
张群, 胡健, 罗迎, 等. 微动目标雷达特征提取、成像与识别研究进展[J]. 雷达学报, 2018, 7(5): 531–547. doi: 10.12000/JR18049
ZHANG Qun, HU Jian, LUO Ying, et al. Research progresses in radar feature extraction, imaging, and recognition of target with micro-motions[J]. Journal of Radars, 2018, 7(5): 531–547. doi: 10.12000/JR18049
|
[30] |
KEBE M, GADHAFI R, MOHAMMAD B, et al. Human vital signs detection methods and potential using radars: A review[J]. Sensors, 2020, 20(5): 1454. doi: 10.3390/s20051454
|
[31] |
龙腾, 毛二可, 何佩琨. 调频步进雷达信号分析与处理[J]. 电子学报, 1998, 26(12): 84–88. doi: 10.3321/j.issn:0372-2112.1998.12.019
LONG Teng, MAO Erke, and HE Peikun. Analysis and processing of modulated frequency stepped radar signal[J]. Acta Electronica Sinica, 1998, 26(12): 84–88. doi: 10.3321/j.issn:0372-2112.1998.12.019
|
[32] |
LV Hao, JIAO Teng, ZHANG Yang, et al. A novel method for breath detection via stepped-frequency continuous wave ultra-wideband (SFCW UWB) radars based on operational bandwidth segmentation[J]. Sensors, 2018, 18(11): 3873. doi: 10.3390/s18113873
|
[33] |
张杨, 焦腾, 荆西京, 等. 生物雷达技术的研究现状与新进展[J]. 信息化研究, 2010, 36(10): 6–10, 13. doi: 10.3969/j.issn.1674-4888.2010.10.002
ZHANG Yang, JIAO Teng, JING Xijing, et al. Current state and progress of the technology of bioradar[J]. Informatization Research, 2010, 36(10): 6–10, 13. doi: 10.3969/j.issn.1674-4888.2010.10.002
|
[34] |
黄文奎. 毫米波汽车防撞雷达的设计与实现[D]. [博士论文], 中国科学院研究生院, 2006.
HUANG Wenkui. Design and production of millimeter-wave automotive radar for collision avoidance application[D]. [Ph. D. dissertation], University of Chinese Academy of Sciences, 2006.
|
[35] |
赵锴. 汽车防碰撞系统雷达设计与信号处理[D]. [硕士论文], 青岛理工大学, 2018.
ZHAO Kai. Design and signal processing on automotive anti-collision radar system[D]. [Master dissertation], Qingdao University of Technology, 2018.
|
[36] |
胡程, 廖鑫, 向寅, 等. 一种生命探测雷达微多普勒测量灵敏度分析新方法[J]. 雷达学报, 2016, 5(5): 455–461. doi: 10.12000/JR16090
HU Cheng, LIAO Xin, XIANG Yin, et al. Novel analytic method for determining micro-Doppler measurement sensitivity in life-detection radar[J]. Journal of Radars, 2016, 5(5): 455–461. doi: 10.12000/JR16090
|
[37] |
DING Chuanwei, HONG Hong, ZOU Yu, et al. Continuous human motion recognition with a dynamic range-Doppler trajectory method based on FMCW radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 6821–6831. doi: 10.1109/TGRS.2019.2908758
|
[38] |
赵珍珍. 老年人跌倒检测算法的研究现状[J/OL]. 计算机工程与应用, http://kns.cnki.net/kcms/detail/11.2127.tp.20211112.0903.002.html. 2021.
ZHAO Zhenzhen. Research status of elderly fall detection algorithms[J]. Computer Engineering and Applications, http://kns.cnki.net/kcms/detail/11.2127.tp.20211112.0903.002.html. 2021.
|
[39] |
HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. The 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016: 770–778.
|
[40] |
LI Haobo, SHRESTHA A, HEIDARI H, et al. Bi-LSTM network for multimodal continuous human activity recognition and fall detection[J]. IEEE Sensors Journal, 2020, 20(3): 1191–1201. doi: 10.1109/JSEN.2019.2946095
|
[41] |
ALANAZI M A, ALHAZMI A K, YAKOPCIC C, et al. Machine learning models for human fall detection using millimeter wave sensor[C]. 2021 55th Annual Conference on Information Sciences and Systems (CISS), Baltimore, USA, 2021: 1–5.
|
[42] |
BHATTACHARYA A and VAUGHAN R. Deep learning radar design for breathing and fall detection[J]. IEEE Sensors Journal, 2020, 20(9): 5072–5085. doi: 10.1109/JSEN.2020.2967100
|
[43] |
韩文婷, 娄昊, 樊阳, 等. 一种改进的 MIMO 生物雷达人体目标检测跟踪联合自适应算法[J]. 信号处理, 2021, 37(11): 2227–2234. doi: 10.16798/j.issn.1003-0530.2021.11.025
HAN Wenting, LOU Hao, FAN Yang, et al. An improved joint adaptive algorithm for MIMO bio-radar human target detection and tracking[J]. Journal of Signal Processing, 2021, 37(11): 2227–2234. doi: 10.16798/j.issn.1003-0530.2021.11.025
|
[44] |
ADIB F, KABELAC Z, KATABI D, et al. 3D tracking via body radio reflections[C]. The 11th USENIX Conference on Networked Systems Design and Implementation, Seattle, USA, 2014: 317–329.
|
[45] |
WANG Fengyu, ZENG Xiaolu, WU Chenshu, et al. MmHRV: Contactless heart rate variability monitoring using millimeter-wave radio[J]. IEEE Internet of Things Journal, 2021, 8(22): 16623–16636. doi: 10.1109/JIOT.2021.3075167
|
[46] |
JIN Feng, SENGUPTA A, and CAO Siyang. MmFall: Fall detection using 4-D mmWave radar and a hybrid variational RNN autoencoder[J]. IEEE Transactions on Automation Science and Engineering, 2020: 1–13. doi: 10.1109/TASE.2020.3042158
|
[47] |
GUPTA S, RAI P K, KUMAR A, et al. Target classification by mmWave FMCW radars using machine learning on range-angle images[J]. IEEE Sensors Journal, 2021, 21(18): 19993–20001. doi: 10.1109/JSEN.2021.3092583
|
[48] |
ZHANG Feng, WU Chenshu, WANG Beibei, et al. MmEye: Super-resolution millimeter wave imaging[J]. IEEE Internet of Things Journal, 2021, 8(8): 6995–7008. doi: 10.1109/JIOT.2020.3037836
|
[49] |
HSU C Y, HRISTOV R, LEE G H, et al. Enabling identification and behavioral sensing in homes using radio reflections[C]. The 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK, 2019: 548.
|
[50] |
OUAKNINE A, NEWSON A, REBUT J, et al. CARRADA dataset: Camera and automotive radar with range-angle-Doppler annotations[C]. 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021: 5068–5075.
|
[51] |
陈功, 张业荣. 基于胶囊内窥镜的胃部肿瘤检测方法?[J]. 物理学报, 2016, 65(16): 194101. doi: 10.7498/aps.65.194101
CHEN Gong and ZHANG Yerong. A method of detecting stomach tumour based on capsule endoscopy[J]. Acta Physica Sinica, 2016, 65(16): 194101. doi: 10.7498/aps.65.194101
|
[52] |
REIMER T, SACRISTAN J, and PISTORIUS S. Improving the diagnostic capability of microwave radar imaging systems using machine learning[C]. 2019 13th European Conference on Antennas and Propagation (EuCAP), Krakow, Poland, 2019: 1–5.
|
[53] |
OMER M, MOJABI P, KURRANT D, et al. Proof-of-concept of the incorporation of ultrasound-derived structural information into microwave radar imaging[J]. IEEE Journal on Multiscale and Multiphysics Computational Techniques, 2018, 3: 129–139. doi: 10.1109/JMMCT.2018.2865111
|
[54] |
INAN O T, MIGEOTTE P F, PARK K S, et al. Ballistocardiography and seismocardiography: A review of recent advances[J]. IEEE Journal of Biomedical and Health Informatics, 2015, 19(4): 1414–1427. doi: 10.1109/JBHI.2014.2361732
|
[55] |
TAEBI A, SOLAR B E, BOMAR A J, et al. Recent advances in seismocardiography[J]. Vibration, 2019, 2(1): 64–86. doi: 10.3390/vibration2010005
|
[56] |
MERCURI M, LORATO I R, LIU Yaohong, et al. Vital-sign monitoring and spatial tracking of multiple people using a contactless radar-based sensor[J]. Nature Electronics, 2019, 2(6): 252–262. doi: 10.1038/s41928-019-0258-6
|
[57] |
MERCURI M, LU Yiting, POLITO S, et al. Enabling robust radar-based localization and vital signs monitoring in multipath propagation environments[J]. IEEE Transactions on Biomedical Engineering, 2021, 68(11): 3228–3240. doi: 10.1109/TBME.2021.3066876
|
[58] |
CHEN Feng, JIANG Xiaonan, JEONG M G, et al. Multitarget vital signs measurement with chest motion imaging based on MIMO radar[J]. IEEE Transactions on Microwave Theory and Techniques, 2021, 69(11): 4735–4747. doi: 10.1109/TMTT.2021.3076239
|
[59] |
SUN Li, HUANG Shuaiming, LI Yusheng, et al. Remote measurement of human vital signs based on joint-range adaptive EEMD[J]. IEEE Access, 2020, 8: 68514–68524. doi: 10.1109/ACCESS.2020.2985286
|
[60] |
TODA D, ANZAI R, ICHIGE K, et al. ECG signal reconstruction using FMCW radar and convolutional neural network[C]. 2021 20th International Symposium on Communications and Information Technologies (ISCIT), Tottori, Japan, 2021: 176–181.
|
[61] |
HA U, ASSANA S, and ADIB F. Contactless seismocardiography via deep learning radars[C]. The 26th Annual International Conference on Mobile Computing and Networking, London, United Kingdom, 2020: 62.
|
[62] |
RONNEBERGER O, FISCHER P, and BROX T. U-net: Convolutional networks for biomedical image segmentation[C]. 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015: 234–241.
|
[63] |
ZHENG Tianyue, CHEN Zhe, CAI Chao, et al. V2iFi: In-vehicle vital sign monitoring via compact RF sensing[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020, 4(2): 70. doi: 10.1145/3397321
|
[64] |
CHEN Zhe, ZHENG Tianyue, CAI Chao, et al. MoVi-Fi: Motion-robust vital signs waveform recovery via deep interpreted RF sensing[C]. The 27th Annual International Conference on Mobile Computing and Networking, New Orleans, USA, 2021: 392–405.
|
[65] |
AHMAD A, ROH J C, WANG Dan, et al. Vital signs monitoring of multiple people using a FMCW millimeter-wave sensor[C]. 2018 IEEE Radar Conference (RadarConf18), Oklahoma City, USA, 2018: 1450–1455.
|
[66] |
MERCURI M, SACCO G, HORNUNG R, et al. 2-D localization, angular separation and vital signs monitoring using a SISO FMCW radar for smart long-term health monitoring environments[J]. IEEE Internet of Things Journal, 2021, 8(14): 11065–11077. doi: 10.1109/JIOT.2021.3051580
|
[67] |
胡锡坤, 金添. 基于自适应小波尺度选择的生物雷达呼吸与心跳分离方法[J]. 雷达学报, 2016, 5(5): 462–469. doi: 10.12000/JR16103
HU Xikun and JIN Tian. Adaptive wavelet scale selection-based method for separating respiration and heartbeat in bio-radars[J]. Journal of Radars, 2016, 5(5): 462–469. doi: 10.12000/JR16103
|
[68] |
HE Mi, NIAN Yongjian, and GONG Yushun. Novel signal processing method for vital sign monitoring using FMCW radar[J]. Biomedical Signal Processing and Control, 2017, 33: 335–345. doi: 10.1016/j.bspc.2016.12.008
|
[69] |
DRAGOMIRETSKIY K and ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531–544. doi: 10.1109/TSP.2013.2288675
|
[70] |
RIBEIRO A H, RIBEIRO M H, PAIXÃO G M M, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network[J]. Nature Communications, 2020, 11(1): 1760. doi: 10.1038/s41467-020-15432-4
|
[71] |
ZHAO Mingmin, ADIB F, and KATABI D. Emotion recognition using wireless signals[C]. The 22nd Annual International Conference on Mobile Computing and Networking, New York, USA, 2016: 95–108.
|
[72] |
UR REHMAN N and AFTAB H. Multivariate variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2019, 67(23): 6039–6052. doi: 10.1109/TSP.2019.2951223
|
[73] |
CHEN Ting, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations[C]. The 37th International Conference on Machine Learning, Vienna, Austria, 2020: 1597–1607.
|
[74] |
祁富贵, 岳超, 梁福来, 等. SFCW生物雷达人体细粒度运动信号微多普勒特征增强方法研究[J]. 中国医疗设备, 2016, 31(2): 39–43, 94. doi: 10.3969/j.issn.1674-1633.2016.02.009
QI Fugui, YUE Chao, LIANG Fulai, et al. A study on the micro-Doppler signature enhanced technique for the finer-grained human activity signal acquired by the SFCW bio-radar[J]. China Medical Devices, 2016, 31(2): 39–43, 94. doi: 10.3969/j.issn.1674-1633.2016.02.009
|
[75] |
张杨, 吕昊, 于霄, 等. 基于超宽谱雷达多目标穿墙探测定位技术的研究[J]. 医疗卫生装备, 2016, 37(8): 10–13. doi: 10.7687/J.ISSN1003-8868.2016.08.010
ZHANG Yang, LYU Hao, YU Xiao, et al. Research of through-wall detection and location technique for multihuman targets using ultra wideband radar[J]. Chinese Medical Equipment Journal, 2016, 37(8): 10–13. doi: 10.7687/J.ISSN1003-8868.2016.08.010
|
[76] |
MA Yangyang, WANG Pengfei, XUE Huijun, et al. Non-contact vital states identification of trapped living bodies using ultra-wideband bio-radar[J]. IEEE Access, 2020, 9: 6550–6559. doi: 10.1109/ACCESS.2020.3048381
|
[77] |
LIANG Fulai, LI Haonan, LIU Miao, et al. Autofocusing method for through-the-wall bioradar imagery of human vital signs[J]. The Journal of Engineering, 2019, 2019(21): 7597–7600. doi: 10.1049/joe.2019.0540
|
[78] |
李廉林, 崔铁军. 智能电磁感知的若干进展[J]. 雷达学报, 2021, 10(2): 183–190. doi: 10.12000/JR21049
LI Lianlin and CUI Tiejun. Recent progress in intelligent electromagnetic sensing[J]. Journal of Radars, 2021, 10(2): 183–190. doi: 10.12000/JR21049
|
[79] |
LI Lianlin, SHUANG Ya, MA Qian, et al. Intelligent metasurface imager and recognizer[J]. Light:Science & Applications, 2019, 8(1): 97. doi: 10.1038/s41377-019-0209-z
|
[80] |
LIU Zhenyu, KONG Yongan, ZHANG Xin, et al. Vital sign extraction in the presence of radar mutual interference[J]. IEEE Signal Processing Letters, 2020, 27: 1745–1749. doi: 10.1109/LSP.2020.3026942
|
[81] |
ZHANG Yang, QI Fugui, LV Hao, et al. Bioradar technology: Recent research and advancements[J]. IEEE Microwave Magazine, 2019, 20(8): 58–73. doi: 10.1109/MMM.2019.2915491
|
[82] |
HUANG Xinming, SUN Ling, TIAN Tian, et al. Real-time non-contact infant respiratory monitoring using UWB radar[C]. 2015 IEEE 16th International Conference on Communication Technology (ICCT), Hangzhou, China, 2015: 493–496.
|
[83] |
LI Chuantao, CHEN Fuming, JIN Jingxi, et al. A method for remotely sensing vital signs of human subjects outdoors[J]. Sensors, 2015, 15(7): 14830–14844. doi: 10.3390/s150714830
|
[84] |
王健琪, 薛慧君, 吕昊, 等. 非接触生理信号检测技术[J]. 中国医疗设备, 2013, 28(11): 5–8, 80. doi: 10.3969/j.issn.1674-1633.2013.11.002
WANG Jianqi, XUE Huijun, LV Hao, et al. Non-contact detection technology for physiological signals[J]. Chinese Medical Devices, 2013, 28(11): 5–8, 80. doi: 10.3969/j.issn.1674-1633.2013.11.002
|
[85] |
KUMAR S S, DASHTIPOUR K, ABBASI Q H, et al. A review on wearable and contactless sensing for COVID-19 with policy challenges[J]. Frontiers in Communications and Networks, 2021, 2: 636293. doi: 10.3389/frcmn.2021.636293
|
[86] |
王健琪, 王海滨, 荆西京, 等. 呼吸、心率的雷达式非接触检测系统设计与研究[J]. 中国医疗器械杂志, 2001, 25(3): 132–135.
WANG Jianqi, WANG Haibing, JING Xijing, et al. The study on non-contact detection of breathing and heartbeat based on radar principles[J] Chinese Journal of Medical Instrumentation, 2001, 25(3): 132–135.
|
[87] |
SCHMIECH D, MULLER S, and DIEWALD A R. 4-channel I/Q-radar system for vital sign monitoring in a baby incubator[C]. 2018 19th International Radar Symposium (IRS), Bonn, Germany, 2018: 1–9.
|
[88] |
SUN Guanghao, OKADA M, NAKAMURA R, et al. Twenty‐four‐hour continuous and remote monitoring of respiratory rate using a medical radar system for the early detection of pneumonia in symptomatic elderly bedridden hospitalized patients[J]. Clinical Case Reports, 2019, 7(1): 83–86. doi: 10.1002/ccr3.1922
|
[89] |
ADHIKARI A, HETHERINGTON A, and SUR S. MmFlow: Facilitating at-home spirometry with 5G smart devices[C]. 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Rome, Italy, 2021: 1–9.
|
[90] |
JORGENSEN G, DOWNEY C, GOLDIN J, et al. An australasian commentary on the AASM manual for the scoring of sleep and associated events[J]. Sleep and Biological Rhythms, 2020, 18(3): 163–185. doi: 10.1007/s41105-020-00259-9
|
[91] |
BONNET M H and ARAND D L. Heart rate variability: Sleep stage, time of night, and arousal influences[J]. Electroencephalography and Clinical Neurophysiology, 1997, 102(5): 390–396. doi: 10.1016/S0921-884X(96)96070-1
|
[92] |
彭小虎, 王国锋, 刘军, 等. 睡眠微观结构—CAP与睡眠质量评估[J]. 中国临床心理学杂志, 2013, 21(6): 920–923. doi: 10.16128/J.CNKI.1005-3611.2013.06.030
PENG Xiaohu, WANG Guofeng, LIU Jun, et al. Sleep microstructure—CAP and sleep quality assessment[J]. Chinese Journal of Clinical Psychology, 2013, 21(6): 920–923. doi: 10.16128/J.CNKI.1005-3611.2013.06.030
|
[93] |
FONSECA P, DEN TEULING N, LONG Xi, et al. Cardiorespiratory sleep stage detection using conditional random fields[J]. IEEE Journal of Biomedical and Health Informatics, 2017, 21(4): 956–966. doi: 10.1109/JBHI.2016.2550104
|
[94] |
SCHULZ S, ADOCHIEI F C, EDU I R, et al. Cardiovascular and cardiorespiratory coupling analyses: A review[J]. Philosophical Transactions. Series A, Mathematical, Physical and Engineering Sciences, 2013, 371(1997): 20120191. doi: 10.1098/rsta.2012.0191
|
[95] |
BARTSCH R P, LIU K K L, MA Q D Y, et al. Three independent forms of cardio-respiratory coupling: Transitions across sleep stages[C]. Computing in Cardiology 2014, Cambridge, USA, 2014: 781–784.
|
[96] |
LONG Xi, FOUSSIER J, FONSECA P, et al. Analyzing respiratory effort amplitude for automated sleep stage classification[J]. Biomedical Signal Processing and Control, 2014, 14: 197–205. doi: 10.1016/j.bspc.2014.08.001
|
[97] |
HSU C Y, AHUJA A, YUE Shichao, et al. Zero-Effort in-home sleep and insomnia monitoring using radio signals[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2017, 1(3): 1–18. doi: 10.1145/3130924
|
[98] |
ZHAO Mingmin, YUE Shichao, KATABI D, et al. Learning sleep stages from radio signals: A conditional adversarial architecture[C]. The 34th International Conference on Machine Learning, Sydney, Australia, 2017: 4100–4109.
|
[99] |
TZENG E, HOFFMAN J, SAENKO K, et al. Adversarial discriminative domain adaptation[C]. The IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 7167–7176.
|
[100] |
GOTTLIEB D J and PUNJABI N M. Diagnosis and management of obstructive sleep apnea: A review[J]. JAMA, 2020, 323(14): 1389–1400. doi: 10.1001/jama.2020.3514
|
[101] |
THOMAS R J, MIETUS J E, PENG C K, et al. Differentiating obstructive from central and complex sleep apnea using an automated electrocardiogram-based method[J]. Sleep, 2007, 30(12): 1756–1769. doi: 10.1093/sleep/30.12.1756
|
[102] |
BABOLI M, SINGH A, SOLL B, et al. Wireless sleep apnea detection using continuous wave quadrature Doppler radar[J]. IEEE Sensors Journal, 2020, 20(1): 538–545. doi: 10.1109/JSEN.2019.2941198
|
[103] |
MENDONÇA F, MOSTAFA S S, RAVELO-GARCIA A G, et al. A review of obstructive sleep apnea detection approaches[J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23(2): 825–837. doi: 10.1109/JBHI.2018.2823265
|
[104] |
NANDAKUMAR R, GOLLAKOTA S, and WATSON N. Contactless sleep apnea detection on smartphones[C]. The 13th Annual International Conference on Mobile Systems, Applications, and Services, Florence, Italy, 2015: 45–57.
|
[105] |
ISLAM S M M, RAHMAN A, YAVARI E, et al. Identity authentication of OSA patients using microwave doppler radar and machine learning classifiers[C]. 2020 IEEE Radio and Wireless Symposium (RWS), San Antonio, USA, 2020: 251–254.
|
[106] |
ARSALAN M, SANTRA A, and WILL C. Improved contactless heartbeat estimation in FMCW radar via Kalman filter tracking[J]. IEEE Sensors Letters, 2020, 4(5): 1–4. doi: 10.1109/LSENS.2020.2983706
|
[107] |
WANG Qisong, DONG Zhening, LIU Dan, et al. Frequency-modulated continuous wave radar respiratory pattern detection technology based on multifeature[J]. Journal of Healthcare Engineering, 2021, 2021: 9376662. doi: 10.1155/2021/9376662
|
[108] |
BAHAMMAM A S, TATE R, MANFREDA J, et al. Upper airway resistance syndrome: Effect of nasal dilation, sleep stage, and sleep position[J]. Sleep, 1999, 22(5): 592–598. doi: 10.1093/sleep/22.5.592
|
[109] |
GU Weixi, SHANGGUAN Longfei, YANG Zheng, et al. Sleep hunter: Towards fine grained sleep stage tracking with smartphones[J]. IEEE Transactions on Mobile Computing, 2016, 15(6): 1514–1527. doi: 10.1109/TMC.2015.2462812
|
[110] |
MENON A and KUMAR M. Influence of body position on severity of obstructive sleep apnea: A systematic review[J]. International Scholarly Research Notices, 2013, 2013: 670381. doi: 10.1155/2013/670381
|
[111] |
UCHINO K, SHIRAISHI M, TANAKA K, et al. Impact of inability to turn in bed assessed by a wearable three-axis accelerometer on patients with Parkinson’s disease[J]. PLoS ONE, 2017, 12(11): e0187616. doi: 10.1371/journal.pone.0187616
|
[112] |
LIEBENTHAL J A, WU Shasha, ROSE S, et al. Association of prone position with sudden unexpected death in epilepsy[J]. Neurology, 2015, 84(7): 703–709. doi: 10.1212/WNL.0000000000001260
|
[113] |
KLOSTER R and ENGELSKJØN T. Sudden unexpected death in epilepsy (SUDEP): A clinical perspective and a search for risk factors[J]. Journal of Neurology, Neurosurgery & Psychiatry, 1999, 67(4): 439–444. doi: 10.1136/jnnp.67.4.439
|
[114] |
ZHOU Tao, XIA Zhaoyang, WANG Xiangfeng, et al. Human sleep posture recognition based on millimeter-wave radar[C]. 2021 Signal Processing Symposium (SPSympo), LODZ, Poland, 2021: 316–321.
|
[115] |
ADIB F, HSU C Y, MAO Hongzi, et al. Capturing the human figure through a wall[J]. ACM Transactions on Graphics (TOG)
|
[116] |
ZHAO Mingmin, LI Tianhong, ABU ALSHEIKH M, et al. Through-wall human pose estimation using radio signals[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7356–7365.
|
[117] |
ZHAO Mingmin, TIAN Yonglong, ZHAO Hang, et al. RF-based 3D skeletons[C]. The 2018 Conference of the ACM Special Interest Group on Data Communication, Budapest, Hungary, 2018: 267–281.
|
[118] |
YUE Shichao, YANG Yezhe, WANG Hao, et al. BodyCompass: Monitoring sleep posture with wireless signals[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020, 4(2): 1–25. doi: 10.1145/3397311
|
[119] |
RUBENSTEIN L Z. Falls in older people: Epidemiology, risk factors and strategies for prevention[J]. Age and Ageing, 2006, 35(S2): ii37–ii41. doi: 10.1093/ageing/afl084
|
[120] |
HOPKINS J. Falls Cost U. S. Hospitals $34 billion in direct medical costs[EB/OL]. https://www.johnshopkinssolutions.com/article/falls-cost-u-s-hospitals-30-billion-in-direct-medical-costs/, 2015.
|
[121] |
KANNUS P, SIEVÄNEN H, PALVANEN M, et al. Prevention of falls and consequent injuries in elderly people[J]. The Lancet, 2005, 366(9500): 1885–1893. doi: 10.1016/S0140-6736(05)67604-0
|
[122] |
FLEMING J and BRAYNE C. Inability to get up after falling, subsequent time on floor, and summoning help: Prospective cohort study in people over 90[J]. BMJ, 2008, 337: a2227. doi: 10.1136/bmj.a2227
|
[123] |
MUDRAZIJA S, ANGEL J L, CIPIN I, et al. Living alone in the United States and Europe: The impact of public support on the independence of older adults[J]. Research on Aging, 2020, 42(5/6): 150–162. doi: 10.1177/0164027520907332
|
[124] |
GURBUZ S Z and AMIN M G. Radar-based human-motion recognition with deep learning: Promising applications for indoor monitoring[J]. IEEE Signal Processing Magazine, 2019, 36(4): 16–28. doi: 10.1109/MSP.2018.2890128
|
[125] |
JOKANOVIĆ B and AMIN M. Fall detection using deep learning in range-Doppler radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(1): 180–189. doi: 10.1109/TAES.2017.2740098
|
[126] |
元志安, 周笑宇, 刘心溥, 等. 基于RDSNet的毫米波雷达人体跌倒检测方法[J]. 雷达学报, 2021, 10(4): 656–664. doi: 10.12000/JR21015
YUAN Zhian, ZHOU Xiaoyu, LIU Xinpu, et al. Human fall detection method using millimeter-wave radar based on RDSNet[J]. Journal of Radars, 2021, 10(4): 656–664. doi: 10.12000/JR21015
|
[127] |
WANG Bo, GUO Liang, ZHANG Hao, et al. A millimetre-wave radar-based fall detection method using line kernel convolutional neural network[J]. IEEE Sensors Journal, 2020, 20(22): 13364–13370. doi: 10.1109/JSEN.2020.3006918
|
[128] |
TIAN Yonglong, LEE G H, HE Hao, et al. RF-based fall monitoring using convolutional neural networks[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2(3): 137. doi: 10.1145/3264947
|
[129] |
DENG Muqing, WANG Cong, TANG Min, et al. Extracting cardiac dynamics within ECG signal for human identification and cardiovascular diseases classification[J]. Neural Networks, 2018, 100: 70–83. doi: 10.1016/j.neunet.2018.01.009
|
[130] |
OJAROUDI M and BILA S. Multiple time-variant targets detection using MIMO radar framework for cerebrovascular monitoring[C]. 2021 15th European Conference on Antennas and Propagation (EuCAP), Dusseldorf, Germany, 2021: 1–5.
|
[131] |
HUANG Kewu, YANG Ting, XU Jianying, et al. Prevalence, risk factors, and management of asthma in China: A national cross-sectional study[J]. The Lancet, 2019, 394(10196): 407–418. doi: 10.1016/S0140-6736(19)31147-X
|
[132] |
WANG Chen, XU Jianying, YANG Lan, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health study): A national cross-sectional study[J]. The Lancet, 2018, 391(10131): 1706–1717. doi: 10.1016/S0140-6736(18)30841-9
|
[133] |
VARON C, MORALES J, LÁZARO J, et al. A comparative study of ECG-derived respiration in ambulatory monitoring using the single-lead ECG[J]. Scientific Reports, 2020, 10(1): 5704. doi: 10.1038/s41598-020-62624-5
|
[134] |
HA U, MADANI S, and ADIB F. WiStress: Contactless stress monitoring using wireless signals[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2021, 5(3): 1–37. doi: 10.1145/3478121
|
[135] |
BAI Yang, GUAN Yu, and NG Wanfai. Fatigue assessment using ECG and actigraphy sensors[C]. The 2020 International Symposium on Wearable Computers, Virtual Event, Mexico, 2020: 12–16.
|
[136] |
DE VRIES J, MICHIELSEN H, VAN HECK G L, et al. Measuring fatigue in sarcoidosis: The Fatigue Assessment Scale (FAS)[J]. British Journal of Health Psychology, 2004, 9(3): 279–291. doi: 10.1348/1359107041557048
|
[137] |
LIU Jie, ZHANG Kai, HE Wei, et al. Non-contact human fatigue assessment system based on millimeter wave radar[C]. 2021 IEEE 4th International Conference on Electronics Technology (ICET), Chengdu, China, 2021: 173–177.
|