Jia Wenyan, Wu Zekun, Ren Yiqiu, Cao Shunxin, Mao Zhi-Hong, Sun Mingui
Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States.
Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.
Front Nutr. 2021 Jan 14;7:519444. doi: 10.3389/fnut.2020.519444. eCollection 2020.
Despite the extreme importance of food intake in human health, it is currently difficult to conduct an objective dietary assessment without individuals' self-report. In recent years, a passive method utilizing a wearable electronic device has emerged. This device acquires food images automatically during the eating process. These images are then analyzed to estimate intakes of calories and nutrients, assisted by advanced computational algorithms. Although this passive method is highly desirable, it has been thwarted by the requirement of a fiducial marker which must be present in the image for a scale reference. The importance of this scale reference is analogous to the importance of the scale bar in a map which determines distances or areas in any geological region covered by the map. Likewise, the sizes or volumes of arbitrary foods on a dining table covered by an image cannot be determined without the scale reference. Currently, the fiducial marker (often a checkerboard card) serves as the scale reference which must be present on the table before taking pictures, requiring human efforts to carry, place and retrieve the fiducial marker manually. In this work, we demonstrate that the fiducial marker can be eliminated if an individual's dining location is fixed and a one-time calibration using a circular plate of known size is performed. When the individual uses another circular plate of an unknown size, our algorithm estimates its radius using the range of pre-calibrated distances between the camera and the plate from which the desired scale reference is determined automatically. Our comparative experiment indicates that the mean absolute percentage error of the proposed estimation method is ~10.73%. Although this error is larger than that of the manual method of 6.68% using a fiducial marker on the table, the new method has a distinctive advantage of eliminating the manual procedure and automatically generating the scale reference.
尽管食物摄入对人类健康极为重要，但目前在没有个人自我报告的情况下，很难进行客观的饮食评估。近年来，一种利用可穿戴电子设备的被动方法应运而生。该设备在进食过程中自动获取食物图像。然后，在先进的计算算法辅助下，对这些图像进行分析，以估计卡路里和营养素的摄入量。尽管这种被动方法非常理想，但它一直受到一个基准标记要求的阻碍，该标记必须出现在图像中以供比例参考。这个比例参考的重要性类似于地图中的比例尺，它决定了地图所覆盖的任何地质区域的距离或面积。同样，如果没有比例参考，就无法确定图像覆盖的餐桌上任意食物的大小或体积。目前，基准标记（通常是棋盘卡）用作比例参考，在拍照前必须放在桌子上，这需要人工手动携带、放置和取回基准标记。在这项工作中，我们证明，如果个人的用餐位置固定，并且使用已知尺寸的圆形盘子进行一次性校准，就可以消除基准标记。当个人使用另一个尺寸未知的圆形盘子时，我们的算法会根据相机与盘子之间预先校准的距离范围来估计其半径，从而自动确定所需的比例参考。我们的对比实验表明，所提出的估计方法的平均绝对百分比误差约为10.73%。虽然这个误差比在桌子上使用基准标记的手动方法的6.68%要大，但新方法具有消除手动操作并自动生成比例参考的独特优势。
