Research on Motion Artifact Elimination Algorithm for Heart Rate Monitoring in Wearable Application

Heart rate estimated using the photoplethysmography signal during intense physical exercise is highly onerous owing to the existence of noise components like motion artifacts in the photoplethysmography signal. In this paper, a de-nosing technique based on a multi-reference signal is proposed, which can accurately estimate heart rate. The proposed method gets accelerometer data and the difference between two photoplethysmography signals to obtain multiple reference signals at first. Then it uses cascaded Normalized Least Mean Square adaptive filters for motion artifact elimination . Finally, it adopts search range and accelerometer power to track spectral peaks. Simulation results show that the proposed method can reduce the influence of motion artifacts and improve the accuracy of heart rate estimation. The margin of error between the estimated heart rate and the true value is 1.5 ± 1.5 BPM.