MyPlate: A Diet Monitoring and Recommender Application

Abstract

Introduction: Diet monitoring is of great interest in practicing a healthy diet to ensure good health by keeping track of the type and amount of food consumed. The food recommender system could provide personalized sug- gestions about food choices that best suit user preferences, making diet monitoring a valuable tool for maintaining good health, preventing diseases, and achieving a balanced lifestyle. Methods: This article presents a diet monitor- ing and recommender Android mobile application utilizing a Mask Region-based Convolutional Neural Network (Mask-RCNN) and portion analysis with a content-based filtering recommender algorithm. The application, named MyPlate, will provide a daily calorie limit and nutrition guidelines based on the user profile record, such as current and ideal weight. At the same time, MyPlate can also perform food image recognition and portion analysis from the picture captured and estimate the total calories. Finally, MyPlate provides a personalized food intake recommen- dation based on the previous intake record. Results: The developed MyPlate application can achieve accurate food image recognition, with the mean average precision at 0.825. Besides that, the proposed MyPlate application can perform portion analysis and provide food recommendations based on intake history at 80% similarity. Conclusion: The proposed MyPlate Android application has shown the effectiveness of incorporating machine intelligence into a diet monitoring technique that can promote healthy eating habits in the community.