Raju Viprav B, Hossain Delwar, Sazonov Edward
Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35401 USA.
IEEE Sens J. 2023 Mar 1;23(5):5391-5400. doi: 10.1109/jsen.2023.3235956. Epub 2023 Jan 24.
Automatic food portion size estimation (FPSE) with minimal user burden is a challenging task. Most of the existing FPSE methods use fiducial markers and/or virtual models as dimensional references. An alternative approach is to estimate the dimensions of the eating containers prior to estimating the portion size. In this article, we propose a wearable sensor system (the automatic ingestion monitor integrated with a ranging sensor) and a related method for the estimation of dimensions of plates and bowls. The contributions of this study are: 1) the model eliminates the need for fiducial markers; 2) the camera system [automatic ingestion monitor version 2 (AIM-2)] is not restricted in terms of positioning relative to the food item; 3) our model accounts for radial lens distortion caused due to lens aberrations; 4) a ranging sensor directly gives the distance between the sensor and the eating surface; 5) the model is not restricted to circular plates; and 6) the proposed system implements a passive method that can be used for assessment of container dimensions with minimum user interaction. The error rates (mean ± std. dev) for dimension estimation were 2.01% ± 4.10% for plate widths/diameters, 2.75% ± 38.11% for bowl heights, and 4.58% ± 6.78% for bowl diameters.
以最小的用户负担进行自动食物份量估计（FPSE）是一项具有挑战性的任务。大多数现有的FPSE方法使用基准标记和/或虚拟模型作为尺寸参考。另一种方法是在估计份量之前先估计进食容器的尺寸。在本文中，我们提出了一种可穿戴传感器系统（集成了测距传感器的自动摄入监测器）以及一种用于估计盘子和碗尺寸的相关方法。本研究的贡献在于：1）该模型无需基准标记；2）相机系统[自动摄入监测器版本2（AIM-2）]在相对于食物的定位方面不受限制；3）我们的模型考虑了由镜头像差引起的径向镜头畸变；4）测距传感器直接给出传感器与进食表面之间的距离；5）该模型不限于圆形盘子；6）所提出的系统实现了一种被动方法，可用于以最少的用户交互来评估容器尺寸。盘子宽度/直径的尺寸估计错误率（平均值±标准差）为2.01%±4.10%，碗高度的错误率为2.75%±38.11%，碗直径的错误率为4.58%±6.78%。
