面向通感一体化的变分模态分解-希尔伯特-黄变换呼吸频率感知算法

杨小龙; 张亭亭; 周牧; 高铭; 童睿轩

doi:10.11999/JEIT240640

面向通感一体化的变分模态分解-希尔伯特-黄变换呼吸频率感知算法

doi: 10.11999/JEIT240640

重庆邮电大学通信与信息工程学院重庆 400065

基金项目: 国家自然科学基金(62101085)，重庆市教委科学技术研究项目(KJQN202400647)，重庆市研究生科研创新项目(CYS240399)

详细信息

作者简介:
杨小龙：男，副教授，硕士生导师，研究方向为通感一体化，无线定位与感知

张亭亭：女，硕士生，研究方向为无线定位与感知

周牧：男，教授，博士生导师，研究方向为无线定位与感知，量子精密测量

高铭：女，博士生，研究方向为通感一体化

童睿轩：男，硕士生，研究方向为无线定位与感知

通讯作者:
周牧　zhoumu@cqupt.edu.cn

中图分类号: TN929.5
计量
- 文章访问数: 184
- HTML全文浏览量: 45
- PDF下载量: 30
- 被引次数: 0
出版历程
- 收稿日期: 2024-07-23
- 修回日期: 2024-12-11
- 网络出版日期: 2024-12-17

Variational Mode Decomposition-Hilbert-Huang Transform Breathing Rate Sensing Algorithm for Integration of Sensing and Communication

School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62101085), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202400647), Chongqing Graduate Student Research Innovation Project (CYS240399)

摘要

摘要: 通感一体化(ISAC)作为一种6G关键技术，将通信和感知功能集成到Wi-Fi设备，为室内人体呼吸频率感知提供一种有效的方法。针对当前基于ISAC的呼吸频率感知存在鲁棒性低和“盲点”的问题，该文提出一种基于信号变分模态分解(VMD)- 希尔伯特-黄变换(HHT)呼吸频率感知算法。首先，选择对环境感知敏感度较强的Wi-Fi链路构建信道状态信息(CSI)比值模型。其次，将滤波后的CSI比值时间序列的各子载波进行投影，结合幅相信息生成不同呼吸模式信号的候选集。再次，对于每一个子载波，根据周期性在候选集中选择一个短期呼吸噪声比最大的候选序列作为最终的呼吸模式，然后设置阈值选择子载波，并对其进行VMD和HHT时频分析，去除人体呼吸频率成分以外的模态分量，并重构剩余模态分量。在此基础上，利用主成分分析(PCA)对所有重构的子载波降维，选择方差贡献率达到99%以上的主成分分量，并使用ReliefF算法重新构建呼吸信号，得到融合信号。最后，对融合信号利用峰值检测算法计算呼吸频率。实验结果表明，该感知方法在会议办公室和走廊两种场景下的平均估计精度超过97%，显著提高了鲁棒性并克服了“盲点”问题，优于其他现有的感知方案。
- 通感一体化 /
- 信道状态信息 /
- 呼吸频率 /
- Hilbert-Huang变换 /
- 变分模态分解
Abstract: Objective Breathing rate is a vital physiological indicator of human health. Abnormal changes in this rate can signify diseases like chronic obstructive pulmonary disease, sleep apnea syndrome, and nocturnal hypoventilation syndrome. Timely and accurate detection of these changes can help identify health risks early, enable professional medical intervention, and optimize treatment timing, thereby improving overall health. However, current detection methods often face limitations due to noise interference and “blind spot” issues, which impact accuracy and robustness. To address these challenges, this paper employs Wi-Fi devices to measure indoor human breathing rates using Integrated Sensing And Communication (ISAC) technology. By combining Variational Modal Decomposition (VMD) and Hilbert-Huang Transform (HHT), a new breathing rate sensing algorithm is proposed. This approach aims to enhance detection accuracy and robustness, resolve the “blind spot” problem in existing technologies, and offer an efficient and reliable solution for health monitoring. Methods Wi-Fi links with high environmental sensitivity were selected to construct the Channel State Information (CSI) ratio model. Subcarriers of the filtered CSI ratio time series were projected, and amplitude and phase information were combined to generate a candidate set of breathing mode signals. For each subcarrier, the sequence with the highest short-term breath noise ratio, determined by periodicity, was identified as the final breath pattern. A threshold was then applied to select relevant subcarriers. Time-frequency analysis using VMD and HHT eliminated modal components unrelated to the human breath rate, and the remaining components were reconstructed. Principal Component Analysis (PCA) was applied for dimensionality reduction, selecting components accounting for over 99% of the variance. The ReliefF algorithm was subsequently used to reconstruct the breath signal into a fused signal, from which the breathing rate was calculated using a peak detection algorithm. Results and Discussions Experiments were conducted in two scenarios: a conference office and a corridor. In both setups, a pair of transceivers was deployed, with a 2-meter distance maintained between the transmitter and receiver. The transmitter used one omnidirectional antenna, and the receiver had three antennas positioned perpendicular to the ground. Participants were seated on the vertical bisector of the Line Of Sight (LOS) path, synchronizing their breathing with a metronome as CSI data were recorded. Each test lasted 1 minute, with a confirmed breathing rate of 16 bpm. System parameters used in the experiments are detailed in Table 1. In the conference office scenario, this paper collected data at various distances from the participant to the transceiver. As illustrated in Figure 9, the Mean Estimation Accuracy (MEA) of our algorithm remains above 97%, even when the participant is 5 meters away. In contrast, the MEA of the other two methods drops by 4% and 5%, respectively. As the sensing distance increases, the multipath effect intensifies, leading to a gradual weakening of the reflected signal and greater noise interference. This impact significantly challenges the breathing detection accuracy of the other methods. The algorithm presented in this paper incorporates a VMD-HHT time-frequency analysis step. This enhancement allows for effective signal decomposition and feature extraction, markedly improving the accuracy of detecting the target breathing signal. Moreover, the method exhibits strong adaptability and robustness, effectively addressing noise interference and multipath effects in complex environments, thus demonstrating more stable performance. In the corridor scenario, we evaluated the algorithm's performance at varying distances. The average absolute error of the algorithm was measured with distances ranging from 2 meters to 5 meters. At 2 meters, the Mean Absolute Error (MAE) recorded was 0.37 bpm, and even at 5 meters, the MAE only increased to 0.45 bpm, remaining below 0.5 bpm. As the distance between the target and transceiver increased from 3 to 5 meters, the MAE gradually rose. This trend is attributed to the further attenuation of the signal reflected from the human target, along with the escalating multipath and signal attenuation effects in the environment. Conclusions The experimental results indicate that the MEA of this sensing method exceeds 97% in both the conference office and corridor scenarios. This effectively addresses the "blind spot" issue present in current technologies. The enhanced accuracy and robustness of the algorithm outperform existing sensing schemes. Moreover, this method broadens the application of ISAC in breathing detection and opens new avenues for developing intelligent health management systems in the future.
- Integrated Sensing And Communications (ISAC) /
- Channel State Information (CSI) /
- Breathing rate /
- Hilbert-Huang Transform (HHT) /
- Variational Mode Decomposition (VMD)

HTML全文

图 1 Wi-Fi信号室内呼吸感知示意图

下载: 全尺寸图片幻灯片

图 2 呼吸频率感知算法系统框图

下载: 全尺寸图片幻灯片

图 3 Wi-Fi链路选择

下载: 全尺寸图片幻灯片

图 4 CSI投影图

下载: 全尺寸图片幻灯片

图 5 VMD-HHT时频分析

下载: 全尺寸图片幻灯片

图 6 实验场景和设备图

下载: 全尺寸图片幻灯片

图 7 步长对复杂度和精度的影响

下载: 全尺寸图片幻灯片

图 8 多天线收发系统实验结果对比

下载: 全尺寸图片幻灯片

图 9 会议办公室实测结果

下载: 全尺寸图片幻灯片

图 10 呼吸频率测量结果CDF图

下载: 全尺寸图片幻灯片

图 11 呼吸频率误差箱型图

下载: 全尺寸图片幻灯片

图 12 5 m时10次估计值

下载: 全尺寸图片幻灯片

图 13 走廊场景实测结果和算法复杂度比较

下载: 全尺寸图片幻灯片

表 1 收发机参数配置

参数	发射机	接收机
模式	Injection	Monitor
信道编号	64(5.32 GHz)
带宽	20 MHz
发包速率	100 包/s
子载波个数	30
子载波编号	–58, –54,···, 54, 58
发射功率	15 dBm

下载: 导出CSV

参考文献(29)

[1]	ISLAM S M M, MOLINAROA N, SILVESTRI S, et al. Respiratory feature extraction for contactless breathing pattern recognition using a single digital camera[J]. IEEE Transactions on Human-Machine Systems, 2023, 53(3): 642–651. doi: 10.1109/THMS.2023.3254895.
[2]	XIE Wangdong, GAN Liangyu, HUANG Leilei, et al. A real-time respiration monitoring system using WiFi sensing based on the concentric circle model[J]. IEEE Transactions on Biomedical Circuits and Systems, 2023, 17(2): 157–168. doi: 10.1109/TBCAS.2022.3229435.
[3]	YEO M, BYUN H, LEE J, et al. Robust method for screening sleep apnea with single-lead ECG using deep residual network: Evaluation with open database and patch-type wearable device data[J]. IEEE Journal of Biomedical and Health Informatics, 2022, 26(11): 5428–5438. doi: 10.1109/JBHI.2022.3203560.
[4]	余永程, 杨华荣, 郑江环, 等. 多导睡眠呼吸监测的应用与效果研究[J]. 贵州医药, 2018, 42(3): 361–362. YU Yongcheng, YANG Huarong, ZHENG Jianghuan, et al. Study on the application and effect of polysomnographic sleep apnea monitoring[J]. Guizhou Medical Journal, 2018, 42(3): 361–362.
[5]	HUANG Xude, TANG Jinbu, LUO Jingchun, et al. A wearable functional near-infrared spectroscopy (fNIRS) system for obstructive sleep apnea assessment[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, 31: 1837–1846. doi: 10.1109/TNSRE.2023.3260303.
[6]	NGUYEN P, ZHANG Xinyu, HALBOWER A, et al. Continuous and fine-grained breathing volume monitoring from afar using wireless signals[C]. The 35th Annual IEEE International Conference on Computer Communications, San Francisco, USA, 2016: 1–9. doi: 10.1109/INFOCOM.2016.7524402.
[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]	HOU Yuxiao, WANG Yawen, and ZHENG Yuanqing. TagBreathe: Monitor breathing with commodity RFID systems[C]. 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, USA, 2017: 404–413. doi: 10.1109/ICDCS.2017.76.
[9]	ABDELNASSER H, HARRAS K A, and YOUSSEF M. UbiBreathe: A ubiquitous non-invasive WiFi-based breathing estimator[C]. The 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Hangzhou, China, 2015: 277–286. doi: 10.1145/2746285.2755969.
[10]	ZHAO Zhe, LIU Ruiqi, and LI Junqiang. Integrated sensing and communication based breath monitoring using 5G network[C]. 2023 International Wireless Communications and Mobile Computing (IWCMC), Marrakesh, Morocco, 2023: 43–47. doi: 10.1109/IWCMC58020.2023.10182512.
[11]	LIU Fan, CUI Yuanhao, MASOUROS C, et al. Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(6): 1728–1767. doi: 10.1109/JSAC.2022.3156632.
[12]	KIM K, KIM J, and JOUNG J. A survey on system configurations of integrated sensing and communication (ISAC) systems[C]. 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, 2022: 1176–1178. doi: 10.1109/ICTC55196.2022.9952602.
[13]	YANG Xiaolong, CAO Ruoyu, ZHOU Mu, et al. Temporal-frequency attention-based human activity recognition using commercial WiFi devices[J]. IEEE Access, 2020, 8: 137758–137769. doi: 10.1109/ACCESS.2020.3012021.
[14]	YANG Runming, YANG Xiaolong, WANG Jiacheng, et al. Decimeter level indoor localization using WiFi channel state information[J]. IEEE Sensors Journal, 2022, 22(6): 4940–4950. doi: 10.1109/JSEN.2021.3067144.
[15]	YANG Xiaolong, LI Quanchen, ZHOU Mu, et al. Phase-calibration-based 3-D beamspace matrix pencil algorithm for indoor passive positioning and tracking[J]. IEEE Sensors Journal, 2023, 23(17): 19670–19683. doi: 10.1109/JSEN.2023.3295370.
[16]	LIU Xuefeng, CAO Jiannong, TANG Shaojie, et al. Wi-sleep: Contactless sleep monitoring via WiFi signals[C]. 2014 IEEE Real-Time Systems Symposium, Rome, Italy, 2014: 346–355. doi: 10.1109/RTSS.2014.30.
[17]	LIU Jian, WANG Yan, CHEN Yingying, et al. Tracking vital signs during sleep leveraging off-the-shelf WiFi[C]. Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Hangzhou, China, 2015: 267–276. doi: 10.1145/2746285.2746303.
[18]	ZENG Youwei, WU Dan, GAO Ruiyang, et al. FullBreathe: Full human respiration detection exploiting complementarity of CSI phase and amplitude of WiFi signals[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2(3): 148. doi: 10.1145/3264958.
[19]	ZENG Youwei, WU Dan, XIONG Jie, et al. FarSense: Pushing the range limit of WiFi-based respiration sensing with CSI ratio of two antennas[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2019, 3(3): 121. doi: 10.1145/3351279.
[20]	YU Xin, YANG Xiaolong, ZHOU Mu, et al. Wi-breath: Monitoring sleep state with Wi-Fi devices and estimating respiratory rate[C]. The 9th International Conference on Communications, Signal Processing, and Systems, Singapore, 2021: 839–842. doi: 10.1007/978-981-15-8411-4_111.
[21]	WANG Hao, ZHANG Daqing, MA Junyi, et al. Human respiration detection with commodity WiFi devices: Do user location and body orientation matter?[C]. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 2016: 25–36. doi: 10.1145/2971648.2971744.
[22]	WANG Wei, LIU A X, SHAHZAD M, et al. Device-free human activity recognition using commercial WiFi devices[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(5): 1118–1131. doi: 10.1109/JSAC.2017.2679658.
[23]	YANG Jieming, LIU Yanming, LIU Zhiying, et al. A framework for human activity recognition based on WiFi CSI signal enhancement[J]. International Journal of Antennas and Propagation, 2021, 2021: 6654752. doi: 10.1155/2021/6654752.
[24]	LI Xiang, LI Shengjie, ZHANG Daqing, et al. Dynamic-MUSIC: Accurate device-free indoor localization[C]. The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 2016: 196–207. doi: 10.1145/2971648.2971665.
[25]	HALPERIN D, HU Wenjun, SHETH A, et al. Tool release: Gathering 802.11n traces with channel state information[J]. ACM SIGCOMM Computer Communication Review, 2011, 41(1): 53. doi: 10.1145/1925861.1925870.
[26]	WU Dan, ZHANG Daqing, XU Chenren, et al. WiDir: Walking direction estimation using wireless signals[C]. The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 2016: 351–362. doi: 10.1145/2971648.2971658.
[27]	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.
[28]	陶凯, 吴定会. 基于VMD-JAYA-LSSVM的短期风电功率预测[J]. 控制工程, 2021, 28(6): 1143–1149. doi: 10.14107/j.cnki.kzgc.20190288. TAO Kai and WU Dinghui. Short-term wind power prediction based on VMD-JAYA-LSSVM[J]. Control Engineering of China, 2021, 28(6): 1143–1149. doi: 10.14107/j.cnki.kzgc.20190288.
[29]	ZHUO Hongyang, WU Xianda, ZHONG Qinghua, et al. Position-free breath detection during sleep via commodity WiFi[J]. IEEE Sensors Journal, 2023, 23(20): 24874–24884. doi: 10.1109/JSEN.2023.3309839.