采用门控Transformer的非接触式氧气监测
Contactless Oxygen Monitoring with Gated Transformer
摘要：
随着远程医疗的日益普及，确保在家中准确监测基本生理信号变得至关重要，同时将患者开销降至最低。在本文中，我们提出了一种非接触式的方法来监测病人的血氧在家里，简单地通过分析在房间里的无线电信号，没有任何可穿戴设备。我们从病人身体反射回来的无线电信号中提取出病人的呼吸，并设计了一种新颖的神经网络，它可以从病人的呼吸信号中推断出病人的氧气估计值。我们的模型称为\emph{Gated BERT-UNet}，旨在适应患者的医疗指标（例如，性别、睡眠阶段）。它有多个预测头，并通过由人的生理指标控制的门选择最合适的头。大量的实证结果表明，我们的模型在医疗和无线电数据集上都达到了很高的准确性。
英文摘要：
With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead. In this paper, we propose a contactless approach for monitoring patients' blood oxygen at home, simply by analyzing the radio signals in the room, without any wearable devices. We extract the patients' respiration from the radio signals that bounce off their bodies and devise a novel neural network that infers a patient's oxygen estimates from their breathing signal. Our model, called \emph{Gated BERT-UNet}, is designed to adapt to the patient's medical indices (e.g., gender, sleep stages). It has multiple predictive heads and selects the most suitable head via a gate controlled by the person's physiological indices. Extensive empirical results show that our model achieves high accuracy on both medical and radio datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
