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首页> 外文会议>2018 IEEE International Microwave Biomedical Conference >A Supervised Learning Approach for Real Time Vital Sign Radar Harmonics Cancellation
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A Supervised Learning Approach for Real Time Vital Sign Radar Harmonics Cancellation

机译：实时生命体征雷达谐波消除的监督学习方法

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摘要

Vital signs radar has proven to be an interesting and useful tool; however it is still limited by a few key problems. One of these is the generation of harmonics due to nonlinearities arising from the large signal amplitude of respiration when compared to that of heartbeat. As a result, harmonics arise in the spectrum which confound accurate measurement of either. The gamma filter is a supervised machine learning based approach that offers a calibration-free and computationally efficient solution for many nonlinear filtering applications. Here, it is demonstrated for the first time as a tool for real-time heart rate estimation using the baseband signal from a non-contact vital sign signal measured from a 5.8-GHz quadrature Doppler radar. Experimental results show that the proposed filter for removing respiration harmonics can accurately measure heart rate even if it is weak or overwhelmed by the respiratory movement.
机译：生命体征雷达已被证明是一种有趣且有用的工具。但是它仍然受到一些关键问题的限制。其中之一是与心跳相比，由于呼吸的大信号幅度引起的非线性导致的谐波产生。结果，在频谱中产生谐波，这混淆了二者的精确测量。伽马滤波器是一种基于监督的机器学习方法，可为许多非线性滤波应用提供无校准且计算效率高的解决方案。在这里，首次展示了它是一种实时心率估计工具，它使用来自5.8 GHz正交多普勒雷达测量的非接触生命体征信号的基带信号。实验结果表明，所提出的用于消除呼吸谐波的滤波器即使在呼吸运动微弱或不堪重负的情况下也能准确测量心率。

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