Removal of Motion Artifacts in Photoplethysmograph Sensors during Intensive Exercise for Accurate Heart Rate Calculation Based on Frequency Estimation and Notch Filtering

Guoxing Wang,Min Wang,Zhe Li,Qirui Zhang

doi:10.3390/s19153312

Guoxing Wang, Min Wang + Show 2 more

Open Access

https://doi.org/10.3390/s19153312

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Abstract

With photoplethysmograph (PPG) sensors showing increasing potential in wearable health monitoring, the challenging problem of motion artifact (MA) removal during intensive exercise has become a popular research topic. In this study, a novel method that combines heart rate frequency (HRF) estimation and notch filtering is proposed. The proposed method applies a cascaded adaptive noise cancellation (ANC) based on the least mean squares (LMS)-Newton algorithm for preliminary motion artifacts reduction, and further adopts special heart rate frequency tracking and correction schemes for accurate HRF estimation. Finally, notch filters are employed to restore the PPG signal with estimated HRF based on its quasi-periodicity. On an open source data set that features intensive running exercise, the proposed method achieves a competitive mean average absolute error (AAE) result of 0.92 bpm for HR estimation. The practical experiments are carried out with the PPG evaluation platform developed by ourselves. Under three different intensive motion patterns, a 0.89 bpm average AAE result is achieved with the average correlation coefficient between recovered PPG signal and reference PPG signal reaching 0.86. The experimental results demonstrate the effectiveness of the proposed method for accurate HR estimation and robust MA removal in PPG during intensive exercise.

Highlights

Photoplethysmography (PPG) has proven effective in monitoring cardiovascular-related physiological signs, especially heart rate (HR), oxygen saturation (SpO2 ) and blood pressure (BP) [1].Due to the advantages of low cost and convenience, proposed method to restoreofphotoplethysmography (PPG) sensors are widely applied in wearable healthcare
The correlation between two PPG signal sequences S1 and S2 can be calculated using Equation (6), where L is the length of the output PPG sequence. μ1 and μ2 are the averages of S1 and S2 . σ1 and σ2 are the standard deviations
It limits the application of PPG sensors

Summary

Introduction

Due to the advantages of low cost and convenience, PPG sensors are widely applied in wearable healthcare. Though having a great potential for wearable healthcare, the accuracy of PPG sensors during motion such as exercise by the user is still unsatisfactory due to motion artifacts (MA) [2]. The MA is typically caused by the change of blood flow velocity induced by the motion [3] or the relative movement between PPG sensors and human skin [4]. The wide frequency range of MA with time-varying nature makes it difficult to use traditional filtering techniques for the removal of motion artifacts [5]. To improve the reliability and accuracy of health monitors based on PPG sensors, the removal of MA continues to be a technical challenge that needs to be tackled

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Results

Conclusion