CycleGAN Based Motion Artifact Cancellation for Photoplethysmography Wearable Device

Nguyen Mai Hoang Long, Jong Jin Kim, Boon Giin Lee, Wan Young Chung

Research output: Chapter in Book/Conference proceedingBook Chapterpeer-review

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.

Original languageEnglish
Title of host publicationIntelligent Human Computer Interaction - 13th International Conference, IHCI 2021, Revised Selected Papers
EditorsJong-Hoon Kim, Javed Khan, Madhusudan Singh, Uma Shanker Tiwary, Marigankar Sur, Dhananjay Singh
PublisherSpringer Science and Business Media Deutschland GmbH
Pages138-144
Number of pages7
Volume13184
ISBN (Print)9783030984038
DOIs
Publication statusPublished - 2022
Event13th International Conference on Intelligent Human Computer Interaction, IHCI 2021 - Kent, United States
Duration: 20 Dec 202122 Dec 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13184 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Intelligent Human Computer Interaction, IHCI 2021
Country/TerritoryUnited States
CityKent
Period20/12/2122/12/21

Keywords

  • Dataset
  • GAN Learning
  • Motion artifact
  • Photoplethysmography
  • Wearable

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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