Robust Heart Rate Monitoring for Quasi-Periodic Motions by Wrist-Type PPG Signals

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Abstract
Heart rate (HR) monitoring using photoplethysmography (PPG) is a promising feature in modern wearable devices. PPG is easily contaminated by motion artifacts (MA), hindering estimation of HR. For quasi-periodic motions, previous works generally focused on a few specific motions, such as walking and fast running. However, they may not work well for many different quasi-periodic motions where MA are very complex. In this paper, a robust HR monitoring scheme for different quasi-periodic motions using wrist-type PPG is proposed, which consists of dictionary learning for signal characteristics learning, human motion recognition for the current motion recognition and dictionary selection, sparse representation-based MA elimination for denoising, and spectral peak tracking for HR-related spectral peak tracking. The proposed scheme is robust to MA caused by different motions and has high accuracy. Experiments on six common quasi-periodic motions showed that the average absolute error of heart rate estimation was 2.40 beat per minute, and also showed that the proposed method is more robust than some state-of-the-art approaches for different motions.
Funding Information
  • Sichuan Science and Technology (2019YFG0122)
  • National Key Research and Development Program of China Stem Cell and Translational Research (2017YFB1003003)
  • National Natural Science Foundation of China (61872061, 61872068)
  • Science & Technology Department of Sichuan Province of China (2018GZ0071)
  • Fundamental Research Funds for the Central Universities (2672018ZYGX2018J014)

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