面向通感一體化的變分模態(tài)分解-希爾伯特-黃變換呼吸頻率感知算法

楊小龍; 張亭亭; 周牧; 高銘; 童睿軒

doi:10.11999/JEIT240640

面向通感一體化的變分模態(tài)分解-希爾伯特-黃變換呼吸頻率感知算法

doi: 10.11999/JEIT240640

重慶郵電大學通信與信息工程學院重慶 400065

基金項目: 國家自然科學基金(62101085)，重慶市教委科學技術(shù)研究項目(KJQN202400647)，重慶市研究生科研創(chuàng)新項目(CYS240399)

詳細信息

作者簡介:
楊小龍：男，副教授，碩士生導師，研究方向為通感一體化，無線定位與感知

張亭亭：女，碩士生，研究方向為無線定位與感知

周牧：男，教授，博士生導師，研究方向為無線定位與感知，量子精密測量

高銘：女，博士生，研究方向為通感一體化

童睿軒：男，碩士生，研究方向為無線定位與感知

通訊作者:
周牧　zhoumu@cqupt.edu.cn

中圖分類號: TN929.5
計量
- 文章訪問數(shù): 217
- HTML全文瀏覽量: 55
- PDF下載量: 38
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-07-23
- 修回日期: 2024-12-11
- 網(wǎng)絡(luò)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: 通感一體化(ISAC)作為一種6G關(guān)鍵技術(shù)，將通信和感知功能集成到Wi-Fi設(shè)備，為室內(nèi)人體呼吸頻率感知提供一種有效的方法。針對當前基于ISAC的呼吸頻率感知存在魯棒性低和“盲點”的問題，該文提出一種基于信號變分模態(tài)分解(VMD)- 希爾伯特-黃變換(HHT)呼吸頻率感知算法。首先，選擇對環(huán)境感知敏感度較強的Wi-Fi鏈路構(gòu)建信道狀態(tài)信息(CSI)比值模型。其次，將濾波后的CSI比值時間序列的各子載波進行投影，結(jié)合幅相信息生成不同呼吸模式信號的候選集。再次，對于每一個子載波，根據(jù)周期性在候選集中選擇一個短期呼吸噪聲比最大的候選序列作為最終的呼吸模式，然后設(shè)置閾值選擇子載波，并對其進行VMD和HHT時頻分析，去除人體呼吸頻率成分以外的模態(tài)分量，并重構(gòu)剩余模態(tài)分量。在此基礎(chǔ)上，利用主成分分析(PCA)對所有重構(gòu)的子載波降維，選擇方差貢獻率達到99%以上的主成分分量，并使用ReliefF算法重新構(gòu)建呼吸信號，得到融合信號。最后，對融合信號利用峰值檢測算法計算呼吸頻率。實驗結(jié)果表明，該感知方法在會議辦公室和走廊兩種場景下的平均估計精度超過97%，顯著提高了魯棒性并克服了“盲點”問題，優(yōu)于其他現(xiàn)有的感知方案。
- 通感一體化 /
- 信道狀態(tài)信息 /
- 呼吸頻率 /
- Hilbert-Huang變換 /
- 變分模態(tài)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

圖 1 Wi-Fi信號室內(nèi)呼吸感知示意圖

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圖 2 呼吸頻率感知算法系統(tǒng)框圖

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圖 3 Wi-Fi鏈路選擇

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圖 4 CSI投影圖

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圖 5 VMD-HHT時頻分析

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圖 6 實驗場景和設(shè)備圖

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圖 7 步長對復雜度和精度的影響

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圖 8 多天線收發(fā)系統(tǒng)實驗結(jié)果對比

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圖 9 會議辦公室實測結(jié)果

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圖 10 呼吸頻率測量結(jié)果CDF圖

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圖 11 呼吸頻率誤差箱型圖

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圖 12 5 m時10次估計值

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圖 13 走廊場景實測結(jié)果和算法復雜度比較

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表 1 收發(fā)機參數(shù)配置

參數(shù)	發(fā)射機	接收機
模式	Injection	Monitor
信道編號	64(5.32 GHz)
帶寬	20 MHz
發(fā)包速率	100 包/s
子載波個數(shù)	30
子載波編號	–58, –54,···, 54, 58
發(fā)射功率	15 dBm

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