A novel motion and Noise Artifacts Reduction Mechanism (MNARM) for wearable PPG-based heart rate extraction

Heart rate can be calculated by Photoplethysmographic (PPG) signal which is one of the most important biological signals in the field of wearable monitoring. But motion artifacts and noise artifacts have such a bad influence on the quality of PPG signal that it is hard to extract pure heart rate. In this paper, a novel Motion and Noise Artifacts Reduction Mechanism (MNARM) is presented to remove motion artifacts and noise artifacts of PPG signals and calculate heart rate in real time based on a wireless, wrist wearable PPG device. MNARM combines NLMS (Normalized Least Mean Square) adaptive filtering algorithm and Mallat algorithm to remove artifacts which is on the different frequency range with heart rate and artifacts which is not respectively. The novelty of MNARM lied in two facts: a 6-axis acceleration signals are employed as the reference signal; and the combination of the two fundamental algorithms reduces the complexity and the amount of calculation. Therefore it is more suitable and effective for the wearable device to extract heart rate. In experimental tests, results indicate that artifacts are greatly eliminated, the device based on MNARM can measure accurate heart rate in real time in the case of subjects´ gender and age vary or exercise intensity varies in limited range.