基于时间编码超表面的跌倒特征模拟与Wi-Fi感知数据集辅助构建

陈少楠 顾家铭 徐超 孙一淼 王思然 陈展野 刘硕 李会东 戴俊彦 何源 程强

陈少楠, 顾家铭, 徐超, 等. 基于时间编码超表面的跌倒特征模拟与Wi-Fi感知数据集辅助构建[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24247
引用本文: 陈少楠, 顾家铭, 徐超, 等. 基于时间编码超表面的跌倒特征模拟与Wi-Fi感知数据集辅助构建[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24247
CHEN Shaonan, GU Jiaming, XU Chao, et al. Fall feature simulation and wi-fi sensing dataset construction based on time-domain digital coding metasurface[J]. Journal of Radars, in press. doi: 10.12000/JR24247
Citation: CHEN Shaonan, GU Jiaming, XU Chao, et al. Fall feature simulation and wi-fi sensing dataset construction based on time-domain digital coding metasurface[J]. Journal of Radars, in press. doi: 10.12000/JR24247

基于时间编码超表面的跌倒特征模拟与Wi-Fi感知数据集辅助构建

DOI: 10.12000/JR24247
基金项目: 国家重点研发计划(2023YFB3811502);国家杰出青年科学基金项目(62225108);中央高校基本科研业务费专项资金(2242022k60003);国家自然科学基金(62288101, 62201139, U22A2001);江苏省前沿引领技术基础研究专项(BK20212002);江苏省应用数学科学研究中心 (BK20233002);中央高校基本科研业务费专项资金(2242024RCB0005, 2242024K30009);“111”项目(111-2-05)
详细信息
    作者简介:

    陈少楠,博士生,主要研究方向为时间编码超表面及其在雷达技术中的应用

    顾家铭,博士生,主要研究方向为无线网络、低功耗物联网、无线感知

    徐 超,本科生,主要研究方向为低功耗物联网、无线感知

    孙一淼,博士生,主要研究方向为无线感知、移动计算和射频计算

    王思然,博士,主要研究方向为信息超材料及其在雷达、目标特性和无线通信中的应用

    陈展野,博士,副教授,主要研究方向为新型电磁调控系统信息处理、雷达数字仿真与数据增广以及雷达运动目标检测

    刘 硕,博士,教授,主要研究方向为新型人工电磁材料

    李会东,博士,副研究员,主要研究方向为新型编码漏波天线、反射(透射)阵天线

    戴俊彦,博士,副教授,主要研究方向为超表面、可重构智能表面、时空调制技术和无线通信系统

    何源,博士,副教授,主要研究方向为物联网、无线网络、移动和普适计算

    程 强,博士,教授,主要研究方向为超材料设计理论及其应用

    通讯作者:

    戴俊彦 junyand@seu.edu.cn

    程强 qiangcheng@seu.edu.cn

  • 责任主编:陈彦 Corresponding Editor: CHEN Yan
  • 中图分类号: TN820

Fall Feature Simulation and Wi-Fi Sensing Dataset Construction Based on Time-Domain Digital Coding Metasurface

Funds: The National Key Research and Development Program of China (2023YFB3811502), The National Science Foundation (NSFC) for Distinguished Young Scholars of China (62225108), The Fundamental Research Funds for the Central Universi-ties (2242022k60003), The National Natural Science Foundation of China (62288101, 62201139, U22A2001), The Jiangsu Province Frontier Leading Technology Basic Research Project (BK20212002), The Jiangsu Provincial Scientific Research Center of Ap-plied Mathematics (BK20233002), The Fundamental Research Funds for the Central Universi-ties (2242024RCB0005, 2242024K30009), The 111 Project (111-2-05)
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  • 摘要: 随着Wi-Fi感知技术在智能健康监测领域的广泛应用,如何构建高质量的数据集成为亟待解决的关键问题。特别是在监测异常行为(如跌倒)时,传统方法依赖于人体的反复实验,这既存在安全隐患,又面临伦理困境。为应对这一挑战,该文提出了一种基于时间编码超表面的辅助数据样本采集方法。通过模拟人体的运动特征,时间编码超表面可以有效替代人体实验,用于辅助构建Wi-Fi感知数据集。为此该文设计了一款具备0~360°全相位调制能力的时间编码超表面验证了该方案的可行性。实验结果表明,超表面生成的信号能够较好地保留人体运动特征,有效补充真实样本,降低数据采集复杂度,并显著提升模型的监测准确性。该方法为Wi-Fi感知技术的数据采集提供了一种创新且可行的解决方案。

     

  • 图  1  基于时间编码数字超表面的人体跌倒行为特征模拟的示意图

    Figure  1.  Schematic diagram of human fall behavior feature simulation based on time-domain digital coding metasurface

    图  2  时间编码超表面的结构及其电磁特性

    Figure  2.  Structure and electromagnetic characteristics of time-domain digital coding metasurface

    图  3  3组实验的场景构建

    Figure  3.  Scene construction of three experimental groups

    图  4  LeNet网络模型架构

    Figure  4.  LeNet network architecture

    图  5  5类动作(分别对应跌倒、跑近、跑远、走近、走远)的时频分析结果

    Figure  5.  Time - frequency analysis results of five types of actions (corresponding to falling down, running closer, running away, approaching and walking away respectively)

    图  6  时间编码超表面生成运动特征信号的质量检测

    Figure  6.  quality detection of motion feature signals generated by time - encoding metasurface

  • [1] REN Yili, WANG Yichao, CHEN Yingying, et al. A vision-based approach for commodity WiFi sensing[C]. The 20th ACM Conference on Embedded Networked Sensor Systems, Boston, USA, 2022: 800–801. doi: 10.1145/3560905.3568068.
    [2] ZHANG Jie, TANG Zhanyong, LI Meng, et al. CrossSense: Towards cross-site and large-scale WiFi sensing[C]. The 24th Annual International Conference on Mobile Computing and Networking, New Delhi, India, 2018: 305–320. doi: 10.1145/3241539.3241570.
    [3] MA Yongsen, ZHOU Gang, and WANG Shuangquan. WiFi sensing with channel state information: A survey[J]. ACM Computing Surveys (CSUR), 2020, 52(3): 46. doi: 10.1145/3310194.
    [4] JIANG Hongbo, CAI Chao, MA Xiaoqiang, et al. Smart home based on WiFi sensing: A survey[J]. IEEE Access, 2018, 6: 13317–13325. doi: 10.1109/ACCESS.2018.2812887.
    [5] SUN Yimiao, HE Yuan, ZHANG Jiacheng, et al. BIFROST: Reinventing WiFi signals based on dispersion effect for accurate indoor localization[C]. The 21st ACM Conference on Embedded Networked Sensor Systems, Istanbul, Turkiye, 2023: 376–389. doi: 10.1145/3625687.3625786.
    [6] CHEN Yulong, GUO Junchen, SUN Yimiao, et al. ElaSe: Enabling real-time elastic sensing resource scheduling in 5G vRAN[C]. 2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS), Guangzhou, China, 2024: 1–10. doi: 10.1109/IWQoS61813.2024.10682934.
    [7] MONJUR M, LIU Jia, XU Jingye, et al. Data distribution dynamics in real-world WiFi-based patient activity monitoring for home healthcare[C]. 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), Orlando, USA, 2024: 228–233. doi: 10.1109/ICHI61247.2024.00037.
    [8] CHEN H H, LIN Chilun, and CHANG C H. WiFi-based detection of human subtle motion for health applications[J]. Bioengineering, 2023, 10(2): 228. doi: 10.3390/bioengineering10020228.
    [9] GU Zhihao, HE Taiwei, WANG Ziqi, et al. Device-free human activity recognition based on dual-channel transformer using WiFi signals[J]. Wireless Communications and Mobile Computing, 2022, 2022(1): 4598460. doi: 10.1155/2022/4598460.
    [10] WANG Hao, ZHANG Daqing, MA Junyi, et al. Human respiration detection with commodity WiFi devices: Do user location and body orientation matter?[C]. 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 2016: 25–36. doi: 10.1145/2971648.2971744.
    [11] SUN Xian, WANG Peijin, YAN Zhiyuan, et al. FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 184: 116–130. doi: 10.1016/j.isprsjprs.2021.12.004.
    [12] ZHANG Wei, TANG Ping, and ZHAO Lijun. Remote sensing image scene classification using CNN-CapsNet[J]. Remote Sensing, 2019, 11(5): 494. doi: 10.3390/rs11050494.
    [13] IGUAL R, MEDRANO C, and PLAZA I. A comparison of public datasets for acceleration-based fall detection[J]. Medical Engineering & Physics, 2015, 37(9): 870–878. doi: 10.1016/j.medengphy.2015.06.009.
    [14] KWOLEK B and KEPSKI M. Human fall detection on embedded platform using depth maps and wireless accelerometer[J]. Computer Methods and Programs in Biomedicine, 2014, 117(3): 489–501. doi: 10.1016/j.cmpb.2014.09.005.
    [15] MA Chao, SHIMADA A, UCHIYAMA H, et al. Fall detection using optical level anonymous image sensing system[J]. Optics & Laser Technology, 2019, 110: 44–61. doi: 10.1016/j.optlastec.2018.07.013.
    [16] LIU Mengwei, ZHANG Yujia, WANG Jiachuang, et al. A star-nose-like tactile-olfactory bionic sensing array for robust object recognition in non-visual environments[J]. Nature Communications, 2022, 13(1): 79. doi: 10.1038/s41467-021-27672-z.
    [17] HAO Xuejie, LIU Lu, YANG Rongjin, et al. A review of data augmentation methods of remote sensing image target recognition[J]. Remote Sensing, 2023, 15(3): 827. doi: 10.3390/rs15030827.
    [18] SHORTEN C and KHOSHGOFTAAR T M. A survey on image data augmentation for deep learning[J]. Journal of Big Data, 2019, 6(1): 60. doi: 10.1186/s40537-019-0197-0.
    [19] DUONG H T and NGUYEN-THI T A N. A review: Preprocessing techniques and data augmentation for sentiment analysis[J]. Computational Social Networks, 2021, 8(1): 1. doi: 10.1186/s40649-020-00080-x.
    [20] GAO Xin, DENG Fang, and YUE Xianghu. Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty[J]. Neurocomputing, 2020, 396: 487–494. doi: 10.1016/j.neucom.2018.10.109.
    [21] LAN Lan, YOU Lei, ZHANG Zeyang, et al. Generative adversarial networks and its applications in biomedical informatics[J]. Frontiers in Public Health, 2020, 8: 164. doi: 10.3389/fpubh.2020.00164.
    [22] WANG Siran, CHEN Mingzheng, KE Junchen, et al. Asynchronous space-time-coding digital metasurface[J]. Advanced Science, 2022, 9(24): 2200106. doi: 10.1002/advs.202200106.
    [23] ZHOU Qunyan, DAI Junyan, FANG Zuqi, et al. Generalized high-precision and wide-angle DOA estimation method based on space-time-coding digital metasurfaces[J]. IEEE Internet of Things Journal, 2024, 11(23): 38196–38206. doi: 10.1109/JIOT.2024.3445451.
    [24] ZHOU Qunyan, WU Junwei, WANG Siran, et al. Two-dimensional direction-of-arrival estimation based on time-domain-coding digital metasurface[J]. Applied Physics Letters, 2022, 121(18): 181702. doi: 10.1063/5.0124291.
    [25] CUI Tiejun, QI Meiqing, WAN Xiang, et al. Coding metamaterials, digital metamaterials and programmable metamaterials[J]. Light: Science & Applications, 2014, 3(10): e218. doi: 10.1038/lsa.2014.99.
    [26] LI Lianlin, CUI Tiejun, JI Wei, et al. Electromagnetic reprogrammable coding-metasurface holograms[J]. Nature Communications, 2017, 8(1): 197. doi: 10.1038/s41467-017-00164-9.
    [27] ZHAO Jie, YANG Xi, DAI Junyan, et al. Programmable time-domain digital-coding metasurface for non-linear harmonic manipulation and new wireless communication systems[J]. National Science Review, 2019, 6(2): 231–238. doi: 10.1093/nsr/nwy135.
    [28] DAI Junyan, YANG Liuxi, KE Junchen, et al. High-efficiency synthesizer for spatial waves based on space-time-coding digital metasurface[J]. Laser & Photonics Reviews, 2020, 14(6): 1900133. doi: 10.1002/lpor.201900133.
    [29] LI Shiyuan, WANG Jianyang, FANG Xinyu, et al. Jamming of ISAR imaging with time-modulated metasurface partially covered on targets[J]. IEEE Antennas and Wireless Propagation Letters, 2023, 22(2): 372–376. doi: 10.1109/LAWP.2022.3212923.
    [30] KE Junchen, CHEN Xiangyu, TANG Wankai, et al. Space-frequency-polarization-division multiplexed wireless communication system using anisotropic space-time-coding digital metasurface[J]. National Science Review, 2022, 9(11): nwac225. doi: 10.21203/rs.3.rs-1509959/v1.
    [31] WANG Siran, DAI Junyan, ZHOU Qunyan, et al. Manipulations of multi-frequency waves and signals via multi-partition asynchronous space-time-coding digital metasurface[J]. Nature Communications, 2023, 14(1): 5377. doi: 10.1038/s41467-023-41031-0.
    [32] KE Junchen, DAI Junyan, ZHANG Junwei, et al. Frequency-modulated continuous waves controlled by space-time-coding metasurface with nonlinearly periodic phases[J]. Light: Science & Applications, 2022, 11(1): 273. doi: 10.1038/s41377-022-00973-8.
    [33] WANG Siran, DAI Junyan, KE Junchen, et al. Radar micro-Doppler signature generation based on time-domain digital coding metasurface[J]. Advanced Science, 2024, 11(19): 2306850. doi: 10.1002/advs.202306850.
    [34] HE Yuan, ZHANG Jia, XI Rui, et al. Detection and identification of non-cooperative UAV using a COTS mmWave radar[J]. ACM Transactions on Sensor Networks, 2024, 20(2): 44. doi: 10.1145/3638767.
    [35] CHEN Yande, HE Yuan, SUN Yimiao, et al. mmTAI: Biometrics-assisted multi-person tracking with mmWave radar[C]. 2024 IEEE 30th International Conference on Parallel and Distributed Systems (ICPADS), Belgrade, Serbia, 2024: 26–33. doi: 10.1109/ICPADS63350.2024.00014.
    [36] ZHANG Jia, XI Rui, HE Yuan, et al. A survey of mmWave-based human sensing: Technology, platforms and applications[J]. IEEE Communications Surveys & Tutorials, 2023, 25(4): 2052–2087. doi: 10.1109/COMST.2023.3298300.
    [37] JAGANATHAN D, BALSUBRAMANIAM S, SURESHKUMAR V, et al. Concatenated modified LeNet approach for classifying pneumonia images[J]. Journal of Personalized Medicine, 2024, 14(3): 328. doi: 10.3390/jpm14030328.
    [38] TANG Chong, VISHWAKARMA S, LI Wenda, et al. Augmenting experimental data with simulations to improve activity classification in healthcare monitoring[C]. 2021 IEEE Radar Conference, Atlanta, USA, 2021: 1–6. doi: 10.1109/RadarConf2147009.2021.9455314.
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  • 收稿日期:  2024-12-11

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