Wearable Data Generation Using Time-Series Generative Adversarial Networks for Hydration Monitoring

Farida Sabry, Wadha Labda, Tamer Eltaras, Fatima Hamza, Khawla Alzoubi, Qutaibah Malluhi

2023

Abstract

Collection of biosignals data from wearable devices for machine learning tasks can sometimes be expensive and time-consuming and may violate privacy policies and regulations. Successful and accurate generation of these signals can help in many wearable devices applications as well as overcoming the privacy concerns accompanied with healthcare data. Generative adversarial networks (GANs) have been used successfully in generating images in data-limited situations. Using GANs for generating other types of data has been actively researched in the last few years. In this paper, we investigate the possibility of using a time-series GAN (TimeGAN) to generate wearable devices data for a hydration monitoring task to predict the last drinking time of a user. Challenges encountered in the case of biosignals generation and state-of-the-art methods for evaluation of the generated signals are discussed. Results have shown the applicability of using TimeGAN for this task based on quantitative and visual qualitative metrics. Limitations on the quality of the generated signals were highlighted with suggesting ways for improvement.

Paper Citation

in Harvard Style

Sabry F., Labda W., Eltaras T., Hamza F., Alzoubi K. and Malluhi Q. (2023). Wearable Data Generation Using Time-Series Generative Adversarial Networks for Hydration Monitoring. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS; ISBN 978-989-758-631-6, SciTePress, pages 94-105. DOI: 10.5220/0011757200003414

in Bibtex Style

@conference{biosignals23,
author={Farida Sabry and Wadha Labda and Tamer Eltaras and Fatima Hamza and Khawla Alzoubi and Qutaibah Malluhi},
title={Wearable Data Generation Using Time-Series Generative Adversarial Networks for Hydration Monitoring},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={94-105},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011757200003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS
TI - Wearable Data Generation Using Time-Series Generative Adversarial Networks for Hydration Monitoring
SN - 978-989-758-631-6
AU - Sabry F.
AU - Labda W.
AU - Eltaras T.
AU - Hamza F.
AU - Alzoubi K.
AU - Malluhi Q.
PY - 2023
SP - 94
EP - 105
DO - 10.5220/0011757200003414
PB - SciTePress