Carlos Augusto da Silva Cunha
Duration: 2022 – 2024
Cised team members
Rui Pedro Amaro Duarte
The alignment of nutrition requirements with food plan creation, follow-up, and adjustment, demands the regular gathering of biometrics, food intake habits, physical activity, and energy consumption data. Analyzing these data in the context of individual objective accomplishment provides the feedback for dynamic adjustment of food plans required to build a nutrition control system. The introduction of sensors for data-gathering activities coupled with artificial intelligence algorithms for creating personalized models has transformed nutrition into an autonomous process that can be realized without or with the minimum intervention of the nutritionist. This process is based on a person’s data with a broader spectrum than those proportioned by the follow-up of traditional nutrition. For that reason, it is potentially also more effective. Also, data availability enables food plan adjustment in shorter cycles, helping reduce the time required to meet individual objectives. This project aims to develop a nutrition control system based on data gathered by sensors (e.g., smartwatches and smart scales) for dynamic food plan adjustment, using machine learning and deep learning algorithms.