Estimation of Fluid Intake Volume from Surface Electromyography Signals: A Comparative Study Between Subject-Specific and Global Regression Techniques

Insufficient fluid intake in older adults is a prevalent and concerning health issue with far-reaching implications. Monitoring fluid intake is particularly important in various healthcare settings, including hospitals, long-term care facilities, and home care, as well as for specific populations such as older adults or individuals with certain medical conditions. 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 as a subject-specific regressor, the root mean square (RMSE) for estimating fluid intake volume using the Mean Absolute Value feature gave 1.37 ± 1.1 ml, and using Support Vector as a global regressor, the RMSE was 2.5 ± 1.2 ml using the Waveform Length feature. When applied as global regressors, SVR gave 6.04 ± 1.7 ml with the Mel Frequency Cepstrum Coefficients feature and 6.36 ± 1.6 ml with the Willison Amplitude feature. Random Forest gave 6.04 ± 1.7 ml with the Willison Amplitude feature. These results indicate a step forward in estimating fluid intake volume based on sEMG for hydration monitoring.