Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Ambient Intelligence Based on IoT for Assisting People with Alzheimer’s Disease Through Context Histories

Version 1 : Received: 30 April 2021 / Approved: 4 May 2021 / Online: 4 May 2021 (13:47:01 CEST)

How to cite: Machado, S.D.; Tavares, J.E.D.R.; Martins, M.G.; Barbosa, J.L.V.; González, G.V.; Leithardt, V.R.Q. Ambient Intelligence Based on IoT for Assisting People with Alzheimer’s Disease Through Context Histories. Preprints 2021, 2021050018 (doi: 10.20944/preprints202105.0018.v1). Machado, S.D.; Tavares, J.E.D.R.; Martins, M.G.; Barbosa, J.L.V.; González, G.V.; Leithardt, V.R.Q. Ambient Intelligence Based on IoT for Assisting People with Alzheimer’s Disease Through Context Histories. Preprints 2021, 2021050018 (doi: 10.20944/preprints202105.0018.v1).

Abstract

The new Internet of Things (IoT) applications are enabling the development of projects that help monitoring people with different diseases in their daily lives. Alzheimer’s is a disease that affects neurological functions and needs support to maintain maximum independence and security of patients during this stage of life, as the cure and reversal of symptoms have not yet been discovered. The IoT-based monitoring system provides the caregivers’ support in monitoring people with Alzheimer’s Disease (AD). This paper presents an ontology-based computational model which receives physiological data from external IoT applications, allowing to identify of potentially dangerous behaviors for patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of Context Histories and Context Prediction, which considering the state of the art, it is the only one that uses analysis of Context Histories to perform predictions. The research also proposes a simulator to generate activities of the daily life of patients allowing the creation of datasets. These datasets were used to evaluate the contributions of the model and were generated according to the standardization of the ontology. The simulator generated 1025 scenarios applied to guide the predictions, which achieved average accurary of 97.44%. The experiments also allowed the learning of 20 relevant lessons on technological, medical and methodological aspects of DCARE that are recorded in this article.

Subject Areas

Ambient Intelligence; Internet of Things; Context; Prediction; Context Histories; Alzheimer’s Disease

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