Abstract
Motion artifacts (MA) in photoplethysmography (PPG) signals is a challenging problem in signal processing today although various methods have been researched and developed. Using deep learning techniques recently has demonstrated their performance to overcome many limitations in traditional ones. In this study, we develop a protocol to build the PPG dataset and a cycleGAN-based model which can use to remove MA from PPG signals at the radial artery. We verified that the assumption of noisy PPG signals is a linear combination of clean PPG and accelerator (ACC) signals is not strong enough. Our evaluation of the CycleGAN model for reconstructing PPG signals at the radial artery which consisted of two opposite phases was feasible but the quality of signals needs more further research.
This work was supported by an National Research Foundation grant of Korea Government (2019R1A2C1089139).
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Long, N.M.H., Kim, J.J., Lee, B.G., Chung, W.Y. (2022). CycleGAN Based Motion Artifact Cancellation for Photoplethysmography Wearable Device. In: Kim, JH., Singh, M., Khan, J., Tiwary, U.S., Sur, M., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2021. Lecture Notes in Computer Science, vol 13184. Springer, Cham. https://doi.org/10.1007/978-3-030-98404-5_13
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DOI: https://doi.org/10.1007/978-3-030-98404-5_13
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