| Summary: | We present a beat-to-beat heart rate tracking algorithm that is designed especially to handle the nonstationary motion artifacts often encountered using photoplethysmographic (PPG) signals acquired from smartwatches or a forehead-worn device, during intense exercise. To date, many algorithms have been based on tracking heart rates during intense exercise using an 8-second average of heart rates, which does not accurately capture the large variation in instantaneous heart rates during exercise. In this paper, we propose a novel technique that can accurately estimate heart rates from wearable PPG signals with subjects running on a treadmill and making other sudden movements. The proposed algorithm includes three parts: 1) time-frequency spectrum estimation of PPG and accelerometer signals, 2) motion artifact removal by subtraction of the time-frequency spectra of the accelerometer signals from the PPG signals, and 3) postprocessing to further reject motion artifact-affected heart rates followed by interpolation of removed heart beats using a cubic spline approach. The proposed approach was compared to one of the recent and most accurate algorithms. The results of the proposed and compared algorithms were evaluated with two datasets (IEEE Signal Processing Cup (N=12) and our own dataset (N=10)) obtained from a smartwatch and a forehead PPG sensor with subjects running on a treadmill. The reference heart rates were obtained from a chest-worn ECG. Our method, using a 12 second windowed segment, resulted in an average absolute error of only 2.94 beats per minute and an average relative error of 2.42 beats per minute, which are a 71% and 94% improvement, respectively, over the compared algorithm.
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