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：利用非接触式毫米波雷达连续监测人体生命体征因其能够穿透衣物并在不同光照条件下工作而具有吸引力。遗憾的是，大多数先前研究要求受试者在监测过程中与雷达传感器保持固定距离并保持静止。这些限制阻碍了雷达生命体征监测在真实场景中的应用。在本文中，我们解决了这些限制，并提出了一种使用毫米波雷达的非接触式生理学启发性鲁棒生命体征监测系统——“Pi-ViMo”。我们首先推导出人体多散射点模型，并引入多个散射的相干合并以增强估计胸壁运动的质量，从而能够对雷达视场内任意位置的受试者进行生命体征估计。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体生命体征。所提出的方法能够在受试者位于雷达视场内任意位置时，在存在微水平随机身体运动的情况下分离呼吸和心跳。在雷达测试平台上的实验表明，对于静止受试者，平均呼吸率误差为6%，心率误差为11.9%；对于在不同随机身体运动下的受试者，平均呼吸率误差为13.5%，心率误差为13.6%。