You are what you eat: So measure what you eat!

Measuring food calorie and nutrition intake on a daily basis is one of the main tools that allows dieticians, doctors, and their patients to control and treat obesity, overweightness, or other food-related health problems. Yet doing this measurement correctly and on a daily basis is challenging and one of the main reasons why diet programs fail. In this article, we look at calorie-intake measurement techniques, and we cover both traditional and newer methods with emphasis on the latter. Among the newly proposed methods, Vision Based Measurement (VBM) [1] has gained a lot of attention, because it makes it very easy for users to measure their food's calories and nutrition by simply taking a picture of their food with their smartphone. However, this still faces challenges, such as achieving higher measurement accuracies, recognizing complex food items such as mixed food, lack of sufficient processing power, etc. When measuring food calories with VBM, recognition of the food is a particularly difficult process because food items have different variations in shape and appearance. Furthermore, the algorithms used for food recognition and classification are computationally intensive. We will cover several solutions and architectures in this article that have been proposed to tackle these challenges.