Estimating Fluid Intake Volume Using a Novel Vision-Based Approach

IF 5.6 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm Pub Date : 2023-11-10 DOI:10.1016/j.irbm.2023.100813

Rachel Cohen , Geoff Fernie , Atena Roshan Fekr

{"title":"Estimating Fluid Intake Volume Using a Novel Vision-Based Approach","authors":"Rachel Cohen , Geoff Fernie , Atena Roshan Fekr","doi":"10.1016/j.irbm.2023.100813","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><p>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.</p></div><div><h3>Objective</h3><p>In this paper, we introduce a novel vision-based method to estimate the amount of fluid consumed.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusion</h3><p>This shows that using video inputs does have the potential to detect and estimate the amount of fluid consumed throughout the day.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1959031823000623/pdfft?md5=da6e7f5e4e01e8004b72ca21df8c9f5d&pid=1-s2.0-S1959031823000623-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Irbm","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1959031823000623","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}

引用次数: 0

Abstract

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.

Abstract Image

查看原文本刊更多论文

用一种新的基于视觉的方法估计液体摄入量

对所有年龄段的人来说，保持水分是保持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和提示用户根据需要喝水是很重要的。以前的文献试图测量液体消耗量，通常使用可穿戴设备或嵌入容器中的传感器。目的介绍一种基于视觉的液体摄取量估算方法。方法对8名被试在模拟的家庭环境中从多个容器中饮水和从事其他活动的数据进行三维卷积神经网络训练。结果表明，在一个算法中可以同时进行饮酒检测和体积摄入估计，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-Fold, MAPE为42.4%，MPE为25.4%。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。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Irbm ENGINEERING, BIOMEDICAL-

CiteScore

10.30

自引率

4.20%

发文量

审稿时长

57 days

期刊介绍： IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …