Machine Learning-Based Smart-Textile for COVID-19 Monitoring

Nkengue Marc Junior, Xianyi Zeng, Ludovic Koehl, Xuyuan Tao, François Dassonville, Nicolas Dumont

2024

Abstract

We propose a new low-cost wearable system to guaranty patient mobility and robust monitoring of COVID-19 using physiological signals. Considering the correlation between two key signals (ECG and PPG), the proposed wearable system will integrate an Variational AutoEncoder (VAE) with self-attention block to reconstruct robust ECG, PPG Red and IR signals from a noisy ECG time series. The model performance is evaluated using the Mean Square Error (MSE), the root-mean-square error (RMSE), Mean Absolute Error (MAE) and the Signal-to-Noise Ratio (SNRoutput) for the signals. With a low MSE, RMSE and MAE, as well as good SNR, the model can generate robust and clean data from the noisy ECG waveform measured by the wearable system. we believe that the proposed wearable system can not only help to provide robust online COVID-19 symptoms monitoring but also for other applications.

Paper Citation

in Harvard Style

Marc Junior N., Zeng X., Koehl L., Tao X., Dassonville F. and Dumont N. (2024). Machine Learning-Based Smart-Textile for COVID-19 Monitoring. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES; ISBN 978-989-758-688-0, SciTePress, pages 172-180. DOI: 10.5220/0012466200003657

in Bibtex Style

@conference{biodevices24,
author={Nkengue Marc Junior and Xianyi Zeng and Ludovic Koehl and Xuyuan Tao and François Dassonville and Nicolas Dumont},
title={Machine Learning-Based Smart-Textile for COVID-19 Monitoring},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES},
year={2024},
pages={172-180},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012466200003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES
TI - Machine Learning-Based Smart-Textile for COVID-19 Monitoring
SN - 978-989-758-688-0
AU - Marc Junior N.
AU - Zeng X.
AU - Koehl L.
AU - Tao X.
AU - Dassonville F.
AU - Dumont N.
PY - 2024
SP - 172
EP - 180
DO - 10.5220/0012466200003657
PB - SciTePress