A Model for Monitoring People with Alzheimer's Disease using Context Histories Analysis
Keywords:Alzheimer's disease, Context Histories, Context Prediction, Internet of Things for Healthcare, Patient monitoring
This article presents a model considering physiological data received from external applications, making it possible to identify dangerous behaviors of patients with Alzheimer's Disease (AD). The main scientific contribution of this work is the specification of a model focusing on AD using the analysis of Context Histories and Context Prediction. DCARE is based on the experimental research method, focused on understanding the disease and finding solutions that minimize its impact on the daily monitoring of patients. In addition, a simulator was created, which generates datasets to perform tests of the model, complementarily an ontology was proposed for the treatment of contexts in the subject of Alzheimer's. This article consists of an extended version of the work published at the Brazilian Symposium on Information Systems (SBSI) in 2021. The scenarios used in the construction of the model were elaborated from interviews with five specialists in the care of AD patients. The tests were performed with a dataset of 1026 samples provisioned by the simulator proposed by this work. The results revealed that the predictions of the model's scenarios reached the objective of the work, achieving an accuracy of 97.44%.
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