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

TY - CHAP

T1 - Estimation of Fluid Intake Volume from Surface Electromyography Signals: A Comparative Study of Seven Regression Techniques

AU - Ismail, Iman Ahmed

AU - Kamavuako, Ernest

PY - 2023

Y1 - 2023

N2 - Insufficient fluid intake in older adults, in particular, is a worrying problem and an actual concern that war- rants 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 Re- gressor, 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.

AB - Insufficient fluid intake in older adults, in particular, is a worrying problem and an actual concern that war- rants 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 Re- gressor, 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.

M3 - Conference paper

SN - 978-989-758-631-6

VL - 4

SP - 118

EP - 124

BT - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS, 118-124, 2023

PB - SCITEPRESS - Science and Technology Publications

CY - Lisbon, Portugal

ER -