Continuous monitoring of human vital signs using non-contact mmWave radars is attractive due to their ability to penetrate garments and operate under different lighting conditions. Unfortunately, most prior research requires subjects to stay at a fixed distance from radar sensors and to remain still during monitoring. These restrictions limit the applications of radar vital sign monitoring in real life scenarios. In this paper, we address these limitations and present "Pi-ViMo", a non-contact Physiology-inspired Robust Vital Sign Monitoring system, using mmWave radars. We first derive a multi-scattering point model for the human body, and introduce a coherent combining of multiple scatterings to enhance the quality of estimated chest-wall movements. It enables vital sign estimations of subjects at any location in a radar's field of view. We then propose a template matching method to extract human vital signs by adopting physical models of respiration and cardiac activities. The proposed method is capable to separate respiration and heartbeat in the presence of micro-level random body movements (RBM) when a subject is at any location within the field of view of a radar. Experiments in a radar testbed show average respiration rate errors of 6% and heart rate errors of 11.9% for the stationary subjects and average errors of 13.5% for respiration rate and 13.6% for heart rate for subjects under different RBMs.

翻译：非接触式毫米波雷达对人体生命体征进行持续监测具有很大的吸引力，因为它们能够穿透衣物并在不同的光照条件下工作。不幸的是，大多数先前的研究需要被监测的人在雷达传感器固定距离处保持静止。这些限制限制了毫米波雷达关键性生命体征监测在现实生活场景中的应用。在本文中，我们解决了这些限制，并提出了一种名为“Pi-ViMo”的非接触式、生理学启发式、稳健的生命体征监测系统，使用毫米波雷达。我们首先推导出一个人体多次散射点模型，并引入了多次散射的相干合并，以增强估计的胸壁运动的质量。它能够估计雷达视野内任何位置的受试者的生命体征。然后，我们提出了一种模板匹配方法，通过采用呼吸和心脏活动的物理模型来提取人类的生命体征。在微级随机身体运动（RBM）存在的情况下，在受试者在雷达视野内的任何位置时，所提出的方法能够分离呼吸和心跳。在雷达测试平台上的实验表明，对于静止的受试者，平均呼吸率误差为6％，心率误差为11.9％，对于经历不同RBMs的受试者，平均误差为13.5％的呼吸率和13.6％的心率。