Adaptive frequency tracking for robust heart rate estimation using wrist-type photoplethysmographic signals during physical exercise

In recent years, wearable photoplethysmographic (PPG) biosensors have emerged as promising tools to monitor heart rate (HR) during physical exercise. However, PPG waveforms are easily corrupted by motion artifacts, rendering HR estimation difficult. In this study, HR was estimated using wrist-type PPG signals. A normalized least-mean-squares (NLMS) algorithm was first used to attenuate motion artifacts and reconstruct multiple PPG waveforms from different combinations of corrupted PPG waveforms and accelerometer (ACC) data. An adaptive band-pass filter was then used to track the common instantaneous frequency component (i.e. HR) of the reconstructed PPG waveforms. Our proposed HR estimation method, which is almost real time, resulted in an average absolute error of 1.71 ± 0.49 beats-per-minute and a Pearson correlation coefficient of 0.994 between the true and the estimated HR values. Importantly, as all ACC-PPG combinations were used for motion artifacts cancellation, no assumption about individual ACC axis contribution was required.