Adaptive motion artifact reducing algorithm for wrist photoplethysmography application

Jingwei Zhao; Guijin Wang; Chenbo Shi

doi:10.1117/12.2228935

28 April 2016 Adaptive motion artifact reducing algorithm for wrist photoplethysmography application

Jingwei Zhao, Guijin Wang, Chenbo Shi

Proceedings Volume 9887, Biophotonics: Photonic Solutions for Better Health Care V; 98873H (2016) https://doi.org/10.1117/12.2228935
Event: SPIE Photonics Europe, 2016, Brussels, Belgium

Abstract

Photoplethysmography (PPG) technology is widely used in wearable heart pulse rate monitoring. It might reveal the potential risks of heart condition and cardiopulmonary function by detecting the cardiac rhythms in physical exercise. However the quality of wrist photoelectric signal is very sensitive to motion artifact since the thicker tissues and the fewer amount of capillaries. Therefore, motion artifact is the major factor that impede the heart rate measurement in the high intensity exercising. One accelerometer and three channels of light with different wavelengths are used in this research to analyze the coupled form of motion artifact. A novel approach is proposed to separate the pulse signal from motion artifact by exploiting their mixing ratio in different optical paths. There are four major steps of our method: preprocessing, motion artifact estimation, adaptive filtering and heart rate calculation. Five healthy young men are participated in the experiment. The speeder in the treadmill is configured as 12km/h, and all subjects would run for 3-10 minutes by swinging the arms naturally. The final result is compared with chest strap. The average of mean square error (MSE) is less than 3 beats per minute (BPM/min). Proposed method performed well in intense physical exercise and shows the great robustness to individuals with different running style and posture.

Citation Download Citation

Jingwei Zhao, Guijin Wang, and Chenbo Shi "Adaptive motion artifact reducing algorithm for wrist photoplethysmography application", Proc. SPIE 9887, Biophotonics: Photonic Solutions for Better Health Care V, 98873H (28 April 2016); https://doi.org/10.1117/12.2228935

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