DCARE: um modelo computacional para acompanhamento de pessoas com a doença de Alzheimer baseado em históricos de contextos e predições de contextos

Abstract

The aging of the population generates the incidence of diseases characteristic of advancing age, among them Alzheimer’s desease. Patients with this disease, which affects brain functions, need support to maintain maximum independence and security during this stage of life, as the cure and reversal of symptoms have not yet been discovered. The daily monitoring technologies are an option tool to minimize the impacts caused in the daily lives of these people, ensuring greater patient safety and so that caregivers can monitor their activities, implementing a certain independence. In this context, this work aims to propose a model for monitoring patients with Alzheimer’s, seeking to synthesize the needs and characteristics that make up a better approach for its validation. The main scientific contribution of this work is the specification of a model that predicts user contexts for monitoring people with dementia during their daily lives, promoting accessibility to a tool for patient health and safety, in addition to contributing to the development of a datasets simulator with scenarios of daily activities of patients with Alzheimer’s disease. Based on the experimental research method, we sought to understand the disease and find solutions to minimize its impact on the daily monitoring of patients. To understand the problem, data on Alzheimer’s and the main difficulties faced by patients were collected through bibliographic research. From this information, the search for technologies that met the specified needs occurred. The functionalities employed were evaluated and points of improvement were identified. The project structure identifies the patient’s physiological data received from an external application, associating them with the model’s ontology, generating the context histories. Following the execution flow, Context Prediction techniques are used, which are based on the Context History data to generate prediction of future behaviors of patients, and perform a danger signal alert to the caregiver. The development of the scenarios used in the construction of the model were developed based on interviews with specialists in care for patients with Alzheimer’s disease. From the tests performed, with the mass of data generated by the developed simulator, called DCARE Dataset Simulator, the results of the predictions showed that the developed model reached the objective of the project, reaching 97.44% of general precision rate