Rui Duarte

Principal Researcher:
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.

Principal Researcher:
Rui Pedro Duarte
Duration: 2022 – 2024

Cised team members
Carlos Augusto da Silva Cunha

For a long time, stress has been considered an important health factor that affects the quality of life. Several studies indicate that excessive and continuous stress can trigger or worsen several diseases, such as cancer and cardiovascular. Stress is a response to a stressor, an external stimulus or condition that causes an emotional, physical, or psychological change in the individual. However, even though momentary stress can be easily identified, it is hard for people to recognize that they are living a stressful life. Multiple sclerosis (MS) is a chronic disease that affects individuals. It has symptoms on diverse levels, such as vision, fatigue, numbness and tingling, muscle spasms, stiffness and weakness, mobility problems, problems with thinking, learning, planning, and even depression and anxiety. With this, a few critical questions need to be raised: Is there any relation between stress and MS exacerbations? What are the relevant biomarkers to identify continuous stress? Are they reliable? Are their measurements little intrusive?
Thus, it is vital to measure stress and identify its types, in order to monitor and help people to deal with the manifestations of stress. It becomes crucial to identify continuous stress, as there are studies that indicate that it has a negative impact, while a systemic stress situation may even be rewarded. The monitoring process of this continuous stress needs to be controlled and with low intrusion in the user, so that it does not have negative effects on their lifestyle. For that, there are several biomarkers to identify stress in people, but not all are recommended. The most indicated biomarkers are heart rate (HR) and electrical conductivity, which can be monitored by devices such as smart watches. With this, it is possible to train artificial intelligence models to create a solution to detect long-term stress, which does not harm people's quality of life.