Estimation of Fluid Intake Volume from Surface Electromyography Signals: A Comparative Study of Seven Regression Techniques

Iman Ismail, Ernest Kamavuako

2023

Abstract

Insufficient fluid intake in older adults, in particular, is a worrying problem and an actual concern that warrants scrutiny. Monitoring fluid intake is essential to avoid dehydration and overhydration problems. This paper presents an investigation to estimate the fluid intake volume using surface Electromyographic (sEMG) sensors. Eleven subjects participated in the experiment, and sEMG recordings of swallows from cups, bottles, and straws were collected. Four features were extracted from the EMG signals. Seven regression algorithms were implemented for quantifying the volume of swallowed fluid: Random Forest (RF), Support Vector Regressor, K-nearest neighbour (KNN), Linear Regressor (LR), Decision Tree (DT), Lasso and Ridge. The mean sip volume across subjects was 14.85 ± 5.05 ml. Results showed that using Random Forest, the root mean square (RMSE) for estimating fluid intake volume using one the Mean Absolute Value feature gave 1.37 ± 1.1 ml. These results indicate a step forward in estimating fluid intake volume based on sEMG for hydration monitoring.

Paper Citation

in Harvard Style

Ismail I. and Kamavuako E. (2023). Estimation of Fluid Intake Volume from Surface Electromyography Signals: A Comparative Study of Seven Regression Techniques. 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 118-124. DOI: 10.5220/0011795600003414

in Bibtex Style

@conference{biosignals23,
author={Iman Ismail and Ernest Kamavuako},
title={Estimation of Fluid Intake Volume from Surface Electromyography Signals: A Comparative Study of Seven Regression Techniques},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={118-124},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011795600003414},
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 - Estimation of Fluid Intake Volume from Surface Electromyography Signals: A Comparative Study of Seven Regression Techniques
SN - 978-989-758-631-6
AU - Ismail I.
AU - Kamavuako E.
PY - 2023
SP - 118
EP - 124
DO - 10.5220/0011795600003414
PB - SciTePress