TY - JOUR
T1 - Motion artifact reduction for wrist-worn photoplethysmograph sensors based on different wavelengths
AU - Zhang, Yifan
AU - Song, Shuang
AU - Vullings, Rik
AU - Biswas, Dwaipayan
AU - Simões-Capela, Neide
AU - van Helleputte, Nick
AU - van Hoof, Chris
AU - Groenendaal, Willemijn
PY - 2019/2/7
Y1 - 2019/2/7
N2 - 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.
AB - 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.
KW - Continuous wavelet transforms
KW - Heart rate
KW - Motion artifacts
KW - Photoplethysmography
KW - heart rate
KW - photoplethysmography
KW - motion artifacts
KW - continuous wavelet transforms
UR - http://www.scopus.com/inward/record.url?scp=85061247424&partnerID=8YFLogxK
U2 - 10.3390/s19030673
DO - 10.3390/s19030673
M3 - Article
C2 - 30736395
AN - SCOPUS:85061247424
SN - 1424-8220
VL - 19
JO - Sensors
JF - Sensors
IS - 3
M1 - 673
ER -