Principal Researcher: Carlos Augusto Cunha
Duration: 2020 – 2022
Cised team members
Valter Alves
Rui Pedro Duarte
Funding
CGD; PV
Alzheimer's disease is a progressive loss of mental function, characterized by degeneration of brain tissue, including loss of nerve cells, accumulation of an abnormal protein, and development of neurofibrillary braids. Alzheimer's patients become dependent on other people, even for the most basic tasks. Controlling feeding and hydrating an Alzheimer's patient is thus a crucial task performed by the person who supports their daily routine, called Informal Caregiver (IC).
Conditions of malnutrition, super nutrition, and dehydration are common in people with diseases causing dementia since the loss of their autonomy also manifests itself in the level of their inability to demonstrate food needs. The support of a nutritionist in the preparation and follow-up of a food plan aligned with the needs of the patient is, therefore, fundamental. The monitoring of the food plan is undoubtedly a process that demands from Cl a lot of discipline and the ability to deal with possible circumstantial adaptations, such as replacing foods prescribed in the food plan with other equivalents or changing the quantity of water consumed as a function of ambient temperature.
This project addresses the problem of the creation and monitoring of diet plans in patients with dementia such as Alzheimer's, through the creation of a computer solution. It allows the creation of nutritional plans by the nutritionists using a Web application and the follow-up of these plans by the ICs through an app to significantly increase the quality of life of the patient. The mobile app will be able to send to the CI notifications regarding proper nutrition and hydration in due moments, accompanied by the control of hydration using an intelligent water bottle, will allow ensuring the compliance of the indications of the nutritionist. Besides, the application will suggest alternatives to plan foods if they are unavailable. Another central feature of the solution is the dynamic adaptation of water administration to the patient as a function of the environmental conditions automatically collected by temperature and humidity sensors.
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 sensors.
