Pi-ViMo:使用毫米波雷达的生理启发的鲁棒生命体征监测

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Internet of Things Pub Date : 2023-03-24 DOI:10.1145/3589347
Bo-yan Zhang, Boyu Jiang, Rong Zheng, Xiaoping Zhang, Jun Yu Li, Q. Xu
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引用次数: 2

摘要

使用非接触式毫米波雷达连续监测人体生命体征是有吸引力的,因为它们能够穿透衣服并在不同的照明条件下工作。不幸的是,大多数先前的研究要求受试者与雷达传感器保持固定距离,并在监测期间保持静止。这些限制限制了雷达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Pi-ViMo,一种使用毫米波雷达的非接触式生理启发的鲁棒生命体征监测系统。我们首先推导了人体的多散射点模型,并引入了多个散射点的相干组合来提高估计胸壁运动的质量。它可以在雷达视野(FoV)的任何位置对目标进行生命体征估计。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。当受试者处于雷达视野范围内的任何位置时,该方法能够在微观随机身体运动(RBM)存在的情况下分离呼吸和心跳。在雷达测试台上进行的实验表明,静止状态下受试者的呼吸速率误差平均为6%,心率误差平均为11.9%,不同rbm下受试者的呼吸速率误差平均为13.5%,心率误差平均为13.6%。
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Pi-ViMo: Physiology-inspired Robust Vital Sign Monitoring using mmWave Radars
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 article, 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 (FoV). 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.
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CiteScore
5.20
自引率
3.70%
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