使用雷达以非接触式非侵入性方法估计人的生命体征在医疗领域中提供了方便的方法，以容易且快速地进行多次健康检查。除了在坐着和睡觉时进行监测外，站立姿势也引起了工业和医疗领域的兴趣。然而，由于身体为了平衡而引起的微运动可能导致错误的呼吸率估计，因此更具挑战性。在这项工作中，我们专注于测量一个站立的人的呼吸频率准确的重呼吸检测和估计的能力。介绍并比较了多种估计方法，包括谱估计、基于深度学习的方法以及采用卡尔曼滤波的自适应峰值选择。与Vernier Go Direct \textsuperscript{\textregistered}呼吸带相比，最新技术显示出最佳性能，绝对误差率为1.5 bpm。
英文摘要：
Estimating human vital signs in a contactless non-invasive method using radar provides a convenient method in the medical field to conduct several health checkups easily and quickly. In addition to monitoring while sitting and sleeping, the standing position has aroused interest for both the industrial and medical fields. However, it is more challenging due to the micro motions induced by the body for balancing that may cause false respiratory rate estimation. In this work, we focus on the measurement of the respiratory rate of a standing person accurately with the capability of heavy breath detection and estimation. Multiple estimation approaches are presented and compared, including spectral estimation, deep-learning-based approaches, and adaptive peak selection with Kalman filtering. The latest technique is showing the best performance with an absolute error rate of 1.5 bpm, when compared to a Vernier Go Direct\textsuperscript{\textregistered} respiration belt.
Fitbeat: COVID-19 Estimation based on Wristband Heart Rate
University of Augsburg（奥格斯堡大学）；University of Cambridge（剑桥大学）；University of Zaragoza（萨拉戈萨大学）；University of Bergamo（贝加莫大学）；Medical University of Graz（格拉茨医科大学）
