Ghosh Tonmoy, McCrory Megan A, Marden Tyson, Higgins Janine, Anderson Alex Kojo, Domfe Christabel Ampong, Jia Wenyan, Lo Benny, Frost Gary, Steiner-Asiedu Matilda, Baranowski Tom, Sun Mingui, Sazonov Edward
Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL, United States.
Department of Health Sciences, Boston University, Boston, MA, United States.
Front Nutr. 2023 Jul 27;10:1191962. doi: 10.3389/fnut.2023.1191962. eCollection 2023.
Dietary assessment is important for understanding nutritional status. Traditional methods of monitoring food intake through self-report such as diet diaries, 24-hour dietary recall, and food frequency questionnaires may be subject to errors and can be time-consuming for the user.
This paper presents a semi-automatic dietary assessment tool we developed - a desktop application called Image to Nutrients (I2N) - to process sensor-detected eating events and images captured during these eating events by a wearable sensor. I2N has the capacity to offer multiple food and nutrient databases (e.g., USDA-SR, FNDDS, USDA Global Branded Food Products Database) for annotating eating episodes and food items. I2N estimates energy intake, nutritional content, and the amount consumed. The components of I2N are three-fold: 1) sensor-guided image review, 2) annotation of food images for nutritional analysis, and 3) access to multiple food databases. Two studies were used to evaluate the feasibility and usefulness of I2N: 1) a US-based study with 30 participants and a total of 60 days of data and 2) a Ghana-based study with 41 participants and a total of 41 days of data).
In both studies, a total of 314 eating episodes were annotated using at least three food databases. Using I2N's sensor-guided image review, the number of images that needed to be reviewed was reduced by 93% and 85% for the two studies, respectively, compared to reviewing all the images.
I2N is a unique tool that allows for simultaneous viewing of food images, sensor-guided image review, and access to multiple databases in one tool, making nutritional analysis of food images efficient. The tool is flexible, allowing for nutritional analysis of images if sensor signals aren't available.
饮食评估对于了解营养状况至关重要。通过自我报告来监测食物摄入量的传统方法，如饮食日记、24小时饮食回顾和食物频率问卷，可能会出现误差，并且对使用者来说可能很耗时。
本文介绍了我们开发的一种半自动饮食评估工具——一个名为“图像转营养成分”（I2N）的桌面应用程序，用于处理传感器检测到的进食事件以及可穿戴传感器在这些进食事件中拍摄的图像。I2N能够提供多个食物和营养数据库（例如美国农业部标准参考数据库、美国农业部食品和营养数据库、美国农业部全球品牌食品数据库），用于标注进食时段和食物项目。I2N可估算能量摄入量、营养成分含量以及摄入量。I2N的组成部分有三个方面：1）传感器引导的图像审查；2）为营养分析对食物图像进行标注；3）访问多个食物数据库。两项研究被用于评估I2N的可行性和实用性：1）一项在美国进行的研究，有30名参与者，共60天的数据；2）一项在加纳进行的研究，有41名参与者，共41天的数据。
在两项研究中，总共使用至少三个食物数据库对314个进食时段进行了标注。与审查所有图像相比，使用I2N的传感器引导图像审查功能后，两项研究中需要审查的图像数量分别减少了93%和85%。
I2N是一种独特的工具，它允许在一个工具中同时查看食物图像、进行传感器引导的图像审查以及访问多个数据库，从而高效地对食物图像进行营养分析。该工具具有灵活性，如果没有传感器信号，也可以对图像进行营养分析。
