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摘要:
基于WiFi感知的呼吸监测具有非接触、低成本和隐私保护性高等优点，已成为当前物联网感知层研究的热点. 然而，现有基于WiFi感知的呼吸监测依赖敏感的信道状态信息，在应用时要求处于静止状态的监测目标不能距离WiFi收发设备过远，并要求不能有处于运动状态的非监测目标的干扰，这些要求制约了WiFi感知在呼吸监测方面的应用推广. 为此，提出了一种适应于动态场景的呼吸监测范围扩大方法FDRadio，尝试从分离动态干扰源、消除环境噪声以及增强动态反射信号功率3个方面提高感知精度和监测范围. 具体而言，首先通过合并多个WiFi信道扩展信道带宽，以提高WiFi感知的空间分辨率，并使用有线直连信道作为参考信道去除硬件噪声. 其次分析了监测范围与环境噪声的关系，并基于时间分集提出一种2级消除环境噪声的方法. 此外设计并实现了一种新颖的权值分配算法，通过合理叠加不同天线的比值信号，最大化动态反射信号功率，从而使处理后的信号对呼吸引起的胸腔微弱起伏具有更强的感知能力. 最后将处理后的信号转换到时域上的功率时延谱，利用监测目标和非监测目标之间信号传播路径的距离差，识别目标的呼吸信号. 在商用嵌入式设备上实现了FDRadio，并进行了一系列实验. 实验结果表明，即使监测人员附近有多个连续移动的非监测目标，FDRadio依然能够在7 m监测范围内保持监测误差小于0.5 bpm.
Abstract:WiFi-based respiratory monitoring becomes a hot spot in the sensing layer of IoT benefiting from non-contact, low cost and high privacy protection. However, current WiFi-based respiratory monitoring methods relay on sensitive channel state information (CSI) samples which requires that single monitoring target keeps static without any moving non-target person and closing to the WiFi transceiver device. These requirements limit the large-scale applications of WiFi-based respiratory monitoring. Therefore, we propose a respiratory monitoring range extension method named FDRadio, which is able to work under dynamic interference scenes. In FDRadio, we improve the accuracy and robustness of respiratory monitoring from three aspects: separating dynamic interference sources, eliminating ambient noise and enhancing power of dynamic reflected signal. Specifically, we first expand the channel bandwidth by combining multiple WiFi channels to improve the spatial resolution of WiFi sensing, and employ wired direct channel to remove the accumulated hardware noise caused by combining channels. Second, we analyze the relationship between monitoring range and ambient noise, and then adopt time diversity techniques to design a two-stage ambient noise deduction process for FDRadio. In addition, we design a novel weight allocation algorithm, which maximizes the dynamic reflected signal power, and enhances the ability to sensing weak chest fluctuation caused by breath. Finally, the processed CSI samples are converted to power delay spectrum (PDP) in time domain. By this, the respiratory signal can be directly extracted from the target person using the distance difference. We implement FDRadio on a commercial embedded devices and conduct a series of experiments. The experimental results show that detection error is less than 0.5 bpm under the 7m available monitoring range, even if multiple moving non-target person exists.
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图 5 叠加前与叠加后环境噪声的分布
Figure 5. Distribution of environmental noise before and after superposition
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图 6 将2个信号直接组合可能会导致动态分量抵消
Figure 6. Direct combination of two signals may result in the cancellation of dynamic components
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图 9 低动态场景下不同方法的检测误差
Figure 9. Detection errors of different methods in low dynamic scenes
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图 10 高动态场景中不同检测距离的检测误差
Figure 10. Detection errors at different detection distances in high dynamic scenes
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图 12 不同场景下不同天线配置的检测误差
Figure 12. Detection errors with different antenna configurations in different scenarios
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图 13 收发设备与用户成不同夹角
Figure 13. Sending and receiving equipment is at different angles from the volunteers
表 1 FDRadio的实时性能
Table 1 Real-Time Performance of FDRadio
ms 步骤 S1 S2 S3 S4 S5 执行时间 26.98 0.11 61.62 7.74 70.8 
下载: 导出CSV
表 2 LOS距离对检测误差的影响
Table 2 Impact of LOS Distance on Detection Error
LOS距离/m 1 1.5 2 2.5 3 检测误差/bpm 0.32 0.26 0.23 0.22 0.35
下载: 导出CSV
表 3 不同位置的检测误差
Table 3 Detection Error at Different Locations
bpm 位置 P1 P2 P3 P4 检测误差 0.43 0.39 0.33 0.38
下载: 导出CSV
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