A Supervised Machine Learning Algorithm for Heart Rate Detection Using Doppler Motion-Sensing Radar


The development of vital sign radar technology has shown to be an effective tool for measuring various physiological processes, such as heartbeat and respiration. In this discipline, there are still various Signal Processing issues, such as overcoming the nonlinearities and harmonics that abound in the power spectrum.

Due to the huge signal amplitude, respiration harmonics distort and overwhelm the detection of heartbeat. The gamma filter, a supervised Machine Learning technique, provides an effective, calibration-free approach for modeling the time series cardiac signal in the presence of breathing and respiration artifacts.

A 5.8-GHz quadrature Doppler radar provides the measured signal, and a modified ECG signal serves as the ground truth for training the filter. The heartbeat is independent and separate from respiration, according to experimental results, and the method may be applied in real time.

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