1Departments of Neurosurgery,Electrical & Computer Engineering,and Bioengineering,University of Pittsburgh,Pittsburgh,PA 15260,USA.
3Priority Research Center for Physical Activity and Nutrition,Faculty of Health and Medicine,The University of Newcastle,Callaghan,New South Wales,Australia.
Public Health Nutr. 2019 May;22(7):1180-1192. doi: 10.1017/S136898001800054X. Epub 2018 Apr 6.
Current approaches to food volume estimation require the person to carry a fiducial marker (e.g. a checkerboard card), to be placed next to the food before taking a picture. This procedure is inconvenient and post-processing of the food picture is time-consuming and sometimes inaccurate. These problems keep people from using the smartphone for self-administered dietary assessment. The current bioengineering study presents a novel smartphone-based imaging approach to table-side estimation of food volume which overcomes current limitations.
We present a new method for food volume estimation without a fiducial marker. Our mathematical model indicates that, using a special picture-taking strategy, the smartphone-based imaging system can be calibrated adequately if the physical length of the smartphone and the output of the motion sensor within the device are known. We also present and test a new virtual reality method for food volume estimation using the International Food Unit™ and a training process for error control.
Our pilot study, with sixty-nine participants and fifteen foods, indicates that the fiducial-marker-free approach is valid and that the training improves estimation accuracy significantly (P0·05).
Elimination of a fiducial marker and application of virtual reality, the International Food Unit™ and an automated training allowed quick food volume estimation and control of the estimation error. The estimated volume could be used to search a nutrient database and determine energy and nutrients in the diet.
目前的食物量估计方法要求使用者携带基准标记物（例如棋盘卡），并将其放在食物旁边再进行拍照。该过程较为繁琐，且食物图像的后期处理既耗时又不准确。这些问题使得人们无法将智能手机用于自主饮食评估。本生物工程研究提出了一种新颖的基于智能手机的成像方法，可在餐桌旁对食物量进行估计，从而克服了当前的局限性。
我们提出了一种无需基准标记物的新食物量估计方法。我们的数学模型表明，如果已知智能手机的物理长度和设备内运动传感器的输出，则可以通过特殊的拍照策略对基于智能手机的成像系统进行充分校准。我们还提出并测试了一种新的基于虚拟现实的国际食物单位（International Food Unit，IFU）和误差控制训练的食物量估计方法。
我们的初步研究共纳入了 69 名参与者和 15 种食物，结果表明无基准标记物方法是有效的，且训练可显著提高估计精度（P<0·05）。
消除基准标记物并应用虚拟现实、国际食物单位和自动训练可以快速估计食物量并控制估计误差。估计的体积可用于搜索营养数据库并确定饮食中的能量和营养素。
