AI 中文总结
针对商用Wi-Fi呼吸监测的子载波感知框架，通过选择敏感子载波、无监督聚类估呼吸率，在呼吸率估计准确率和MAE上优于基线，还可用于呼吸暂停检测，适用于家庭医疗监测。
AI 中文摘要
Wi-Fi传感因普及性已成为家庭医疗中无接触呼吸监测的有前景模态。然而，传统方法通常将信道状态信息（CSI）子载波统一处理，忽略了它们对呼吸运动的异质性响应。为解决这一局限，我们提出一种子载波感知传感框架，该框架表征依赖子载波的呼吸敏感性并利用其进行信息性子载波选择。基于呼吸敏感子载波的统计特性，我们进一步开发了一种基于无监督聚类的呼吸估计算法，用于稳健的呼吸监测。实验结果表明，所提方法达到97%的呼吸率估计准确率，与基线方法相比，平均绝对误差（MAE）降低了0.45次/分钟（bpm）。此外，我们将分析扩展到呼吸暂停检测，推导了一种基于幅度衰减的阈值机制。在呼吸暂停情况下，所提方法的呼吸率估计MAE为1.6 bpm。这些结果证明了所提框架在不同场景下的稳健性和有效性，及其在无接触家庭医疗监测中的潜力。
英文摘要
Wi-Fi sensing has emerged as a promising modality for contact-free respiratory monitoring in home healthcare due to its ubiquity. However, conventional approaches typically treat Channel State Information (CSI) subcarriers uniformly, neglecting their heterogeneous responses to breathing motions. To address this limitation, we propose a subcarrier-aware sensing framework that characterizes subcarrier-dependent respiratory sensitivities and exploits them for informative subcarrier selection. Based on the statistical properties of breathing-sensitive subcarriers, we further develop an unsupervised clustering-based breathing estimation method for robust respiratory monitoring. Experimental results show that the proposed method achieves 97% breathing rate estimation accuracy and reduces the mean absolute error (MAE) by 0.45 breaths per minute (bpm) compared with baseline methods. Furthermore, we extend the analysis to breathing-pause detection, which derives an amplitude-attenuation-based threshold mechanism. Under breathing pauses, the proposed method achieves an MAE of 1.6 bpm in breathing rate estimation. These results demonstrate the robustness and effectiveness of the proposed framework across different scenarios and its potential for contactless home healthcare monitoring.
Comments13 pages, 24 figures
