Menu Recommendation System Using Smart Plates for Well-balanced Diet Habits of Young Children

Abstract

A well-balanced diet habit of a wide variety of foods and adequate nutrition can help to maintain proper growth and healthy life for young children. In Korea, young children aged 3 to 6 years use their own plates to eat lunch served in the kindergarten or childcare facilities. In this paper, we propose a smart plate that can easily measure how much food children have eaten. The smart plate has five load cell sensors to measure the weight of five places. Using them, the amount of food intake can be determined by measuring the weight of food before and after meals, respectively. This helps to know which foods young children prefer and which foods they do not prefer and identifies nutritional deficiencies in them. In addition, long-term accumulated data can be used to predict the growth index of young children. Finally, menus are recommended based on the nutrition facts consumed on a monthly basis by analyzing nutrition ingredients that were insufficient or excessive for children.

Ontology-based Daily Menu Recommendation System for Complementary Food According to Nutritional Needs using Naïve Bayes and TOPSIS

“…Naïve Bayes calculations are influenced by user feedback on recipes that have been tried. The steps to calculate Naïve Bayes in this study are, a) Calculate the probability of a preferred material: The probability is counted by using (9). Laplace (add-one) smoothing is used in the equation to avoid getting zero outcomes for the probability when a new application is used or when a menu has never been selected.…”

“…Based on basic health research [5], in 2013, malnutrition in infants and children in Indonesia reached 19.6%, an increase of 1.7% compared to 2010 (17.9%). This is why some studies were focused on giving food recommendations to children, such as [6][7] [8] [9][10] [11]. Furthermore, few researchers concentrate on a specific period of children's age called the golden period.…”

Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients’ Nutrition Control

“…YOLO takes a different approach than the previous algorithm, which used a single neural network to process the entire image [ 24 ]. This network will divide the image into regions and then predict bounding boxes and probabilities; for each bounding region box, the probability of classifying as objects or not is weighed [ 25 ]. Detection is more complex than classification; classification can recognize objects but cannot tell where they are in the image [ 26 ].…”

Practical Approach to Designing and Implementing a Recommendation System for Healthy Challenges

“…Drawing upon behavioral characteristic data, the system generates various clusters to provide dietary recommendations to each of them. Another study focused on healthy nutrition is that of Namgung et al [35], in which they employed smart plates to assess the eating habits of young children. They weighed the plate before and after each meal to calculate the amount of food consumed by the child.…”