Care Guide System for Caregivers of People with Dementia

Kim, Gyungha; Jeon, Hwawoo; Park, Sung-Kee; Choi, Yong Suk; Lim, Yoonseob

doi:10.1109/embc44109.2020.9176513

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…In this study, the Pellet 2.2 reasoner was used because it has some features that other reasoners do not, such as the ability to work with complex data types and processing rules. In [43], we develop an ontological model for caring for people with dementia and a reasoning system that adaptively produces care guidelines under different circumstances. In this sense, ontological models have important properties when it comes to capacity in terms of inference and computation time.…”

Section: Pellet Reasonermentioning

confidence: 99%

“…The variables are associated with positive values in PC1 (principal components) and PC2. References [43,45] The length of the arrows indicates how much each variable contributes to the calculation of each principal component: Disoriented, Wandering, Depressed, Bored, Nervous, No observation. Next, Figure 7 shows that the categories for the variables No Observation = 30.1%, Disoriented = 26.7%, Wandering = 26%, Depressed = 17.1%, Bored = 0%, Nervous = 0%, reach percentages.…”

Section: Depressedmentioning

confidence: 99%

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Ontological Model in the Identification of Emotional Aspects in Alzheimer Patients

et al. 2023

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The present work describes the development of a conceptual representation model of the domain of the theory of formal grammars and abstract machines through ontological modeling. The main goal is to develop an ontology capable of deriving new knowledge about the mood of an Alzheimer’s patient in the categories of wandering, nervous, depressed, disoriented or bored. The patients are from elderly care centers in Ambato Canton-Ecuador. The population consists of 147 individuals of both sexes, diagnosed with Alzheimer’s disease, with ages ranging from 75 to 89 years. The methods used are the taxonomic levels, the semantic categories and the ontological primitives. All these aspects allow the computational generation of an ontological structure, in addition to the use of the proprietary tool Pellet Reasoner as well as Apache NetBeans from Java for process completion. As a result, an ontological model is generated using its instances and Pellet Reasoner to identify the expected effect. It is noted that the ontologies come from the artificial intelligence domain. In this case, they are represented by aspects of real-world context that relate to common vocabularies for humans and applications working in a domain or area of interest.

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Section: Pellet Reasonermentioning

confidence: 99%

Section: Depressedmentioning

confidence: 99%