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摘要: 2010年以来，基于商用WiFi设备实现非接触式呼吸监测得到了广泛关注。然而，现有的方法受人体反射信号强度制约，通常要求人正面朝向WiFi设备，当人体侧向或背部朝向设备时，胸腔反射信号的减弱使得呼吸监测变得困难。为了解决这个问题，该文提出了一种基于智能反射表面(IRS)的新型呼吸监测系统。该系统利用智能反射表面控制WiFi信号在环境中的传播路径，增强了人体的反射，最终实现了姿势鲁棒地呼吸监测。此外，该系统易于部署，无需事先知道收发天线与智能反射表面的确切位置和相应的环境信息。实验验证，与现有的方法相比，该系统显著改善了不同姿势下人体的呼吸监测效果。Abstract: Since 2010, the utilization of commercial WiFi devices for contact-free respiration monitoring has garnered significant attention. However, existing WiFi-based respiration detection methods are susceptible to constraints imposed by hardware limitations and require the person to directly face the WiFi device. Specifically, signal reflection from the thoracic cavity diminishes when the body is oriented sideways or with the back toward the device, leading to complexities in respiratory monitoring. To mitigate these hardware-associated limitations and enhance robustness, we leveraged the signal-amplifying potential of Intelligent Reflecting Surfaces (IRS) to establish a high-precision respiration detection system. This system capitalizes on IRS technology to manipulate signal propagation within the environment to enhance signal reflection from the body, finally achieving posture-resilient respiratory monitoring. Furthermore, the system can be easily deployed without the prior knowledge of antenna placement or environmental intricacies. Compared with conventional techniques, our experimental results validate that this system markedly enhances respiratory monitoring across various postural configurations in indoor environments.
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图 1 在室内基于智能反射表面辅助的呼吸感知部署场景
Figure 1. Deployment scenario of an indoor respiration sensing system assisted by IRS
图 5 利用时分复用来评估码本(每种颜色代表一个智能反射表面码本)
Figure 5. We utilize time division to evaluate the codebooks (each color represents a codebook)
图 13 人体在TP2位置正对WiFi的检测结果
Figure 13. Different results when the target person at location TP2 faces the WiFi device
图 14 人体在TP2位置左侧对WiFi的检测结果
Figure 14. Different results when the target person at location TP2 left to the WiFi device
图 15 人体在TP2位置右侧对WiFi的检测结果
Figure 15. Different results when the target person at location TP2 right to the WiFi device
图 16 人体在TP1位置背对WiFi的检测结果
Figure 16. Different results when the target person at location TP1 backs to the WiFi device
图 17 目标在距离检测设备4 m处，在系统生成码本和随机码本的实验结果
Figure 17. Results of the codebook generated by the proposed system and the random approach, with the target positioned 4 m away from the detection device
1 智能反射表面码本优化迭代算法
1. IRS codebook optimization iterative algorithm
输入：码本池容量L，码本维度$ {N}_{x} $, $ {N}_{y} $，循环阈值$ {\eta }_{{\mathrm{lowest}}} $，
列表长度$ {N}_{{\mathrm{list}}} $输出：最优智能反射表面码本 1: for k = 1 $\to $ L do 2: S[k,:,:] =threshold(random($ {N}_{x},{N}_{y} $), 0.5·ones($ {N}_{x},{N}_{y} $)) 3: end for 4: $ {{S}}_{{\mathrm{peak}}} $ = evaluateCodebook(S) 5: [$ {{S}}_{{\mathrm{peak}}} $,$ \mathrm{i}\mathrm{d} $] = sort($ {{S}}_{{\mathrm{peak}}} $) 6: $ {{S}}_{{\mathrm{pool}}} $=S($ \mathrm{i}\mathrm{d} $) 7: loopFlag = True 8: while loopFlag == True do 9: $ \delta $ = normalize($ {{S}}_{{\mathrm{peak}}} $) 10: $ {\boldsymbol{P}}_{\mathrm{n}\mathrm{e}\mathrm{w}}=\dfrac{2}{{L}^{2}+L}\displaystyle\sum _{i=1}^{L}{\delta }_{i}{\boldsymbol{p}}_{i} $ 11: for I = 1 $\to\; {N}_{{\mathrm{list}}} $ do 12: $ {{S}}_{{\mathrm{new}}} $[i,:,:] = threshold(random($ {N}_{x},{N}_{y} $),$ {\boldsymbol{P}}_{\mathrm{n}\mathrm{e}\mathrm{w}} $) 13: end for 14: $ {{\mathrm{peak}}}_{{\mathrm{new}}} $ = evaluateCodebook($ {{S}}_{{\mathrm{new}}} $) 15: $ {{\mathrm{id}}}_{{\mathrm{replace}}} $ = find($ {{\mathrm{peak}}}_{{\mathrm{new}}} $< $ {{S}}_{{\mathrm{peak}}} $) 16: $ \eta $ = size($ {{\mathrm{id}}}_{{\mathrm{replace}}} $)/$ {N}_{{\mathrm{list}}} $ 17: if $ \eta $ < $ {\eta }_{{\mathrm{lowest}}} $ then 18: loopFlag = Flase 19: end if 20: end while 21: ($ {{S}}_{{\mathrm{pool}}} $(1,:,:)) 
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表 1 不同方位的呼吸频率估计的平均绝对误差(bpm)
Table 1. The MAE breath rate estimation with different orientations of human (bpm)
方法 正对 左侧对 右侧对 背对 FarSense 0.816 2.327 2.213 2.331 IRS-enabled Breath Tracking 0.706 1.892 1.957 2.101 所提方法 0.664 0.753 0.681 0.787
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表 2 不同受试者的呼吸频率估计的平均绝对误差(bpm)
Table 2. The MAE breath rate estimation in different people (bpm)
方法 受试者1 受试者2 受试者3 FarSense 2.188 1.656 1.767 IRS-enabled Breath Tracking 1.192 1.407 1.404 所提方法 0.657 0.706 0.654
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