Nutrition Control System Based on Short-term Personal Demands

Abstract

Personalized nutrition considers an individual's unique genetic, metabolic, and lifestyle factors to create a customized dietary plan tailored to their needs. People seeking to optimize their health and wellness through diet and lifestyle changes can benefit from technological advances in machine learning and deep learning approaches to create personalized models of nutritional requirements that override traditional food plans. These models will provide users with an unprecedented decision tool for informing them of the impact of specific food intake and exercise on their goals. This article presents the architecture, implementation, and preliminary results of a deep learning-based control system for nutrition. It allows users to understand the impact of their food and exercise immediate choices on their goals while reducing user interaction demands. Preliminary results have shown that it is possible to predict BMI (Body Mass Index) accurately within a time window of three days.