Estimating Fluid Intake Volume Using a Novel Vision-Based Approach

Introduction

Staying hydrated is an essential aspect of good health for people of all ages. Tracking fluid intake is important to ensure proper hydration and prompt users to drink as needed. Previous literature has attempted to measure the amount of fluid consumption, often using wearables or sensors embedded in containers.

Objective

In this paper, we introduce a novel vision-based method to estimate the amount of fluid consumed.

Methods

We trained different 3D Convolutional Neural Networks on data from 8 participants drinking from multiple containers and engaging in other activities in a simulated home environment.

Results

We show that it is possible to perform both drinking detection and volume intake estimation in a single algorithm with a Mean Absolute Percent Error (MAPE) of 28.5% and a Mean Percent Error (MPE) of 2.6% with 10-Fold and a MAPE of 42.4% and MPE of 25.4% for Leave-One-Subject-Out cross validation.

Conclusion

This shows that using video inputs does have the potential to detect and estimate the amount of fluid consumed throughout the day.