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A Supervised Machine Learning Algorithm for Heart-Rate Detection Using Doppler Motion-Sensing Radar
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DOI：10.1109/JERM.2019.2923673.png)
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Abstract
En 中文
The advancement of vital sign radar technology has proven to be a useful tool in assessing various physiological dynamics, including heartbeat and respiration. There remains several signal processing challenges in this field, which include overcoming the nonlinearities and harmonics that populate the power spectrum. Respiration harmonics distort and overwhelm the measurement of heartbeat due to the large signal amplitude. A supervised machine learning algorithm, the gamma filter, offers an efficient, calibration-free solution to model the time series heartbeat signal given respiration and respiration artifacts. The measured signal is provided by a 5.8-GHz quadrature Doppler radar and a modified electrocardiogram signal is used as the ground truth for training the filter. Experimental results show that the heartbeat is independent and separable from respiration and the algorithm can be implemented in real time.
Keywords:
Doppler radar vital signs detection
heart rate (HR)
noncontact continuous wave (CW)
machine learning
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