# An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN

arXiv: https://arxiv.org/abs/2106.11512 · HTML: https://mlsanity.com/paper/2106.11512/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [Safe Linear Thompson Sampling with Side Information](https://mlsanity.com/paper/1911.02156/) (2019)
- [Stage-wise Conservative Linear Bandits](https://mlsanity.com/paper/2010.00081/) (2020)
- [An End-to-End and Accurate PPG-based Respiratory Rate Estimation Approach Using Cycle Generative Adversarial Networks](https://mlsanity.com/paper/2105.00594/) (2021)
- [Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS](https://mlsanity.com/paper/2011.10470/) (2020)
- [Improving Photoplethysmographic Measurements under Motion Artifacts using Artificial Neural Network for Personal Healthcare](https://mlsanity.com/paper/1807.05331/) (2018)

## Built on by (most cited first, 10 of 10)

- [PaPaGei: Open Foundation Models for Optical Physiological Signals](https://mlsanity.com/paper/2410.20542/) (2024)
- [Deep Generative Models for Physiological Signals: A Systematic Literature Review](https://mlsanity.com/paper/2307.06162/) (2023)
- [Tiny-PPG: A Lightweight Deep Neural Network for Real-Time Detection of Motion Artifacts in Photoplethysmogram Signals on Edge Devices](https://mlsanity.com/paper/2305.03308/) (2023)
- [SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals](https://mlsanity.com/paper/2404.15353/) (2024)
- [PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning](https://mlsanity.com/paper/2603.03331/) (2026)
- [Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks](https://mlsanity.com/paper/2201.09976/) (2022)
- [Joint Attention Mechanism Learning to Facilitate Opto-physiological Monitoring during Physical Activity](https://mlsanity.com/paper/2502.09291/) (2025)
- [TAU: Modeling Temporal Consistency Through Temporal Attentive U-Net for PPG Peak Detection](https://mlsanity.com/paper/2503.10733/) (2025)
- [Finger-to-Chest Style Transfer-assisted Deep Learning Method For Photoplethysmogram Waveform Restoration with Timing Preservation](https://mlsanity.com/paper/2503.13496/) (2025)
- [Reliable Physiological Monitoring on the Wrist Using Generative Deep Learning to Address Poor Skin-Sensor Contact](https://mlsanity.com/paper/2504.02735/) (2025)

## Abstract (arXiv)

A photoplethysmography (PPG) is an uncomplicated and inexpensive optical technique widely used in the healthcare domain to extract valuable health-related information, e.g., heart rate variability, blood pressure, and respiration rate. PPG signals can easily be collected continuously and remotely using portable wearable devices. However, these measuring devices are vulnerable to motion artifacts caused by daily life activities. The most common ways to eliminate motion artifacts use extra accelerometer sensors, which suffer from two limitations: i) high power consumption and ii) the need to integrate an accelerometer sensor in a wearable device (which is not required in certain wearables). This paper proposes a low-power non-accelerometer-based PPG motion artifacts removal method outperforming the accuracy of the existing methods. We use Cycle Generative Adversarial Network to reconstruct clean PPG signals from noisy PPG signals. Our novel machine-learning-based technique achieves 9.5 times improvement in motion artifact removal compared to the state-of-the-art without using extra sensors such as an accelerometer.