Not knowing when to stop eating or how much food is too much can lead to many health issues. In iLog, we propose a system which can not only monitor but also create awareness for the user of how much food is too much. iLog provides information on the emotional state of a person along with the classification of eating behaviors to Normal-Eating or Stress-Eating. Chronic stress, uncontrolled or unmonitored food consumption, and obesity are intricately connected, even involving certain neurological adaptations. We propose a deep learning model for edge computing platforms which can automatically detect, classify and quantify the objects from the plate of the user. Three different paradigms where the idea of iLog can be performed are explored in this research. Two different edge platforms have been implemented in iLog. The platforms include mobile, as it is widely used, and a single board computer which can easily be a part of network for executing experiments with iLog-Glasses being the main wearable. The iLog model has produced an overall accuracy of 98% with an average precision of 85.8%.

中文翻译：

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iLog：IoMT 中自动食物摄入量监测和压力检测的智能设备

不知道何时停止进食或吃多少食物太多会导致许多健康问题。在 iLog 中，我们提出了一个系统，该系统不仅可以监控，还可以让用户意识到有多少食物是太多了。iLog 提供有关一个人的情绪状态的信息，以及将饮食行为分类为正常饮食或压力饮食。慢性压力、不受控制或不受监控的食物消耗和肥胖有着错综复杂的联系，甚至涉及某些神经适应。我们为边缘计算平台提出了一种深度学习模型，该模型可以自动检测、分类和量化用户盘子中的物体。本研究探索了三种不同的范式，可以在其中执行 iLog 的想法。iLog 中实现了两种不同的边缘平台。平台包括移动、因为它被广泛使用，以及一个单板计算机，可以很容易地成为网络的一部分，以 iLog-Glasses 作为主要可穿戴设备进行实验。iLog 模型的总体准确率达到 98%，平均准确率为 85.8%。