Estimating Fluid Intake Volume Using a Novel Vision-Based Approach - 17/11/23
, Geoff Fernie a, b, c, Atena Roshan Fekr a, bAbstract |
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.
Il testo completo di questo articolo è disponibile in PDF.Graphical abstract |
Highlights |
• | Dehydration is a serious issue and tracking liquid intake can help prevent it. |
• | Using a vision-based approach is a hands-off way to track liquid intake. |
• | Previous works detected when a drink happens, not estimated the amount consumed. |
• | We examine if vision-based approaches can estimate the amount consumed. |
• | This can lead to a more accurate vision-based fluid tracker. |
Keywords : Artificial neural networks, Computer vision, Depth cameras, Fluid intake monitoring, Image recognition, Intake gesture detection, Video signal processing
Mappa
Vol 44 - N° 6
Articolo 100813- dicembre 2023 Ritorno al numero
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