Duration: 2020 – 2021
Team Members
Carlos Vasconcelos
Funding
CGD; PV
For a nutritionist, the elaboration and monitoring of a food plan in line with the needs of the individual present two critical problems: 1) obtaining biometric data, eating habits and energy consumption to create the food plan, and 2) monitoring and the dynamic adaptation of the food plan.
Sports nutrition is one of the most complex areas of nutrition, since it requires observation of a rather comprehensive set of metrics, encompassing the athlete's physical aspects, physical activity, and eating habits. The use of measuring devices for specific parameters of physical activity represents a common practice among athletes. The integration of data automatically collected by these devices with other data not directly observable, such as dietary habits and subjective metrics, is part of the complexity of creating a global register that can be used by the nutritionist during the development of the plan to feed. Also, at a stage after the preparation of the food plan, the need to adapt it may arise. For example, variations in temperature or physical intensity may involve quick changes in the energy or hydration needs of an individual. In such situations, the data collected by the devices could be used to dynamically adjust the plan and send alerts, informing the sportsman of the need to eat food or water at the right time.
The objectives of this project include the creation of (a) a Web application where the nutritionist can record and follow up on dietary plans and (b) a mobile app for sportspersons, which can collect data provided by smart devices or manually entered, and where they can plan and receive notifications. In scientific terms, the project includes the creation of innovative models for adapting food plans using machine learning algorithms and integration approaches, preprocessing and evaluation of data quality collected by external devices.