Volume estimation of fluid intake using regression models

Abstract

Monitoring of water intake is critical for managing the health and wellness of individuals with various health conditions, including young children, sick adults, the elderly, and individuals seeking better weight control. The research presented in this paper studies the use of different regression methods to estimate water intake using wireless surface electromyography (sEMG). The advantage of using regression is that it can provide more consistent values for different swallow volumes. In addition, the setup reported in this research employs a less controlled environment, providing stronger evidence of the practical feasibility of the used setup. Neural networks-based regression achieved an R2 of 0.99 and a root-mean-squared error of 0.14 and 0.08 after feature selection. The relative immunity of sEMG as a sensing technique and the accuracy levels achieved with the used mobile sEMG device can provide a robust system for volume estimation of fluid intake in real-world situations.