Motion artifact reduction for wrist-worn photoplethysmograph sensors based on different wavelengths

Yifan Zhang, Shuang Song (Corresponding author), Rik Vullings, Dwaipayan Biswas, Neide Simões-Capela, Nick van Helleputte, Chris van Hoof, Willemijn Groenendaal (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

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Long-term heart rate (HR) monitoring by wrist-worn photoplethysmograph (PPG) sensors enables the assessment of health conditions during daily life with high user comfort. However, PPG signals are vulnerable to motion artifacts (MAs), which significantly affect the accuracy of estimated physiological parameters such as HR. This paper proposes a novel modular algorithm framework for MA removal based on different wavelengths for wrist-worn PPG sensors. The framework uses a green PPG signal for HR monitoring and an infrared PPG signal as the motion reference. The proposed framework includes four main steps: motion detection, motion removal using continuous wavelet transform, approximate HR estimation and signal reconstruction. The proposed algorithm is evaluated against an electrocardiogram (ECG) in terms of HR error for a dataset of 6 healthy subjects performing 21 types of motion. The proposed MA removal method reduced the average error in HR estimation from 4.3, 3.0 and 3.8 bpm to 0.6, 1.0 and 2.1 bpm in periodic, random, and continuous non-periodic motion situations, respectively.

Original languageEnglish
Article number673
Number of pages18
Issue number3
Publication statusPublished - 7 Feb 2019


  • Continuous wavelet transforms
  • Heart rate
  • Motion artifacts
  • Photoplethysmography
  • heart rate
  • photoplethysmography
  • motion artifacts
  • continuous wavelet transforms


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