RF-Q: Unsupervised Signal Quality Assessment for Robust RF-based Respiration Monitoring
RF-Q: Unsupervised Signal Quality Assessment for Robust RF-based Respiration Monitoring
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DOI：
10.1145/3580252.3586988
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Zongxing Xie;Ava Nederlander;Isac Park;Fan Ye
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文献类型：
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作者：
Zongxing Xie;Ava Nederlander;Isac Park;Fan Ye
对呼吸的持续监测提供了关于健康状况管理(例如，疾病的进展或恢复)的宝贵见解。射频(RF)技术的最新进步凭借其非侵入性显示了持续呼吸监测的前景，并且比需要频繁充电和连续佩戴的可穿戴解决方案更受欢迎。然而，射频信号容易受到现实生活中不可避免的大型身体运动的影响，这对呼吸监测的稳健性提出了挑战。虽然已经提出了许多现有的方法来实现稳健的基于射频的呼吸监测，但它们对监督数据的依赖限制了它们的广泛适用性。在此背景下，我们提出了一种无监督/自监督模型RF-Q，用于实现稳健的基于RF的呼吸监测的信号质量评估和质量感知估计。RF-Q利用自动编码器(AE)神经网络的重建误差来量化RF信号中呼吸信息的质量，而不需要数据标记。将量化后的信号质量和重构后的信号进行加权融合，可以提高射频呼吸监测的鲁棒性。我们证明，与使用大量标签数据应用各自专业设计的复杂模型不同，仅通过以非监督方式量化信号质量，我们可以显著提高基线的平均端到端(E2e)呼吸频率估计精度2.75的改善比，高于排除失真数据的监督基线方法所获得的1.94的增益。
Continuous monitoring of respiration provides invaluable insights about health status management (e.g., the progression or recovery of diseases). Recent advancements in radio frequency (RF) technologies show promise for continuous respiration monitoring by virtue of their non-invasive nature, and preferred over wearable solutions that require frequent charging and continuous wearing. However, RF signals are susceptible to large body movements, which are inevitable in real life, challenging the robustness of respiration monitoring. While many existing methods have been proposed to achieve robust RF-based respiration monitoring, their reliance on supervised data limits their potential for broad applicability. In this context, we propose, RF-Q, an unsupervised/self-supervised model to achieve signal quality assessment and quality-aware estimation for robust RF-based respiration monitoring. RF-Q uses the reconstruction error of an autoencoder (AE) neural network to quantify the quality of respiratory information in RF signals without the need for data labeling. With the combination of the quantified signal quality and reconstructed signal in a weighted fusion, we are able to achieve improved robustness of RF respiration monitoring. We demonstrate that, instead of applying sophisticated models devised with respective expertise using a considerable amount of labeled data, by just quantifying the signal quality in an unsupervised manner we can significantly boost the average end-to-end (e2e) respiratory rate estimation accuracy of a baseline by an improvement ratio of 2.75, higher than the gain of 1.94 achieved by a supervised baseline method that excludes distorted data.