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

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.

Robust PPG-based Ambulatory Heart Rate Tracking Algorithm

“…The ECG signal was used to derive the reference HR using a standard peak detection method based on the Pan-Tompkins algorithm [17]. 2) Adaptive Filter for Motion Artifact Removal: Adaptive filters are commonly used to remove motion artifacts from PPG signals [10], [18]- [22]. Often, adaptive noise cancellation (ANC) is performed using each accelerometer axis in sequence [10], [20], [21].…”

“…2) Adaptive Filter for Motion Artifact Removal: Adaptive filters are commonly used to remove motion artifacts from PPG signals [10], [18]- [22]. Often, adaptive noise cancellation (ANC) is performed using each accelerometer axis in sequence [10], [20], [21]. However, applying the adaptive filter with all three signals at once allows the filter weights to be properly adjusted with information from all motion vectors simultaneously.…”

“…6) Notch Filter HR Refinement: The final step in the algorithm is to refine the initial estimate of the HR by using a notch filter-based strategy similar to that employed by Wang et al [10]. First, a second-order notch filter is constructed of bandwidth .5 Hz at the estimated HR frequency for each 8-second window.…”

“…Major spectral approaches tend to include motion artifact removal (e.g. Wiener filtering [9], adaptive filtering [10], or spectral subtraction [11]), and conversion to time-frequency representation (e.g. Sparse Signal Reconstruction [12], FFT with phase vocoder [9], or Truncated Fourier Series [13]).…”

A Wristwatch-Based Wireless Sensor Platform for IoT Health Monitoring Applications

“…The test protocol used in the clinical trials specified that the arm be parallel to the ground, as the patient would usually be sitting or lying down. This position is recommended because the PPG devices are heavily impacted by motion artefacts, which are likely to have a significant influence on the quality of the measured data [82][83][84]. In Figure 18, the devices used in the clinical trials are shown, such as a gateway, an optical biosensor, a smartphone, and the developed wristwatch sensor platform are illustrated.…”