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.

中文翻译：

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使用基于视觉的新颖方法估计液体摄入量

介绍

保持水分是所有年龄段的人健康的一个重要方面。跟踪液体摄入量对于确保适当的水合作用并提示用户根据需要喝水非常重要。先前的文献曾尝试测量液体消耗量，通常使用可穿戴设备或嵌入容器中的传感器。

客观的

在本文中，我们介绍了一种基于视觉的新颖方法来估计消耗的液体量。

方法

我们根据 8 名参与者的数据训练了不同的 3D 卷积神经网络，这些参与者从多个容器中饮酒并在模拟家庭环境中参与其他活动。

结果

我们证明，可以在单一算法中执行饮酒检测和摄入量估计，平均绝对百分比误差 (MAPE) 为 28.5%，平均百分比误差 (MPE) 为 2.6%（10 倍），MAPE 为留一受试者交叉验证的结果为 42.4%，MPE 为 25.4%。

结论

这表明使用视频输入确实有可能检测和估计全天消耗的液体量。