Poster Abstract: Contactless Vital Sign Monitoring using Low-Power FMCW Radar

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-05-09 DOI:10.1145/3576842.3589169

Steven Marty, Kanika S. Dheman, M. Magno

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Abstract

Non-contact vital sign monitoring has several advantages over conventional methods, such as comfort, unobtrusiveness, and a reduced risk of infection transmission. Low-power millimeter-wave (mmWave) radars offer a promising solution for contactless vital sign measurement in embedded, battery-operated devices. Reduced power of these sensor systems leads to challenges associated with precise and reliable vital sign monitoring, particularly with regard to heart rate (HR) measurement. This work investigates adaptations of standard signal processing steps to extract HR and respiration rate (RR) from the raw data of a Frequency Modulated Continuous Wave (FMCW) radar signal suitable for low-power operation. By using less than 100 kB of RAM it could fit into a typical Bluetooth low-energy module. Additionally, we evaluate the performance of our algorithms on 10 people including different body types, physical conditions and sex achieving a mean absolute error in HR estimation of two beats per minute and below one breath per minute error for the RR.

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摘要:基于低功耗FMCW雷达的非接触式生命体征监测

与传统方法相比，非接触式生命体征监测有几个优点，如舒适、不显眼和降低感染传播的风险。低功耗毫米波(mmWave)雷达为嵌入式电池供电设备中的非接触式生命体征测量提供了一种很有前途的解决方案。这些传感器系统功率的降低给精确可靠的生命体征监测带来了挑战，特别是在心率(HR)测量方面。这项工作研究了标准信号处理步骤的适应性，以从适合低功耗操作的调频连续波(FMCW)雷达信号的原始数据中提取HR和呼吸速率(RR)。通过使用不到100 kB的内存，它可以适合一个典型的蓝牙低功耗模块。此外，我们评估了我们的算法在10个人(包括不同的身体类型、身体状况和性别)上的性能，实现了HR估计的平均绝对误差为每分钟两次心跳，RR的误差低于每分钟一次呼吸。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation

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