An Ontology-Driven Ambient Assisted Living Framework for Personalized Elderly Care with Health-Risk Inference
DOI:
https://doi.org/10.59796/jcst.V16N3.2026.201Keywords:
ambient assisted living, ontology, SWRL, SPARQL, semantic web, semantic reasoning, knowledge graphAbstract
As the global elderly population increases, there is a growing need for structured long-term care systems that support independent living and explainable health monitoring. This study presents AALHealthIoT, a Web Ontology Language (OWL) 2 DL–based ontology framework for Ambient Assisted Living (AAL) that integrates physiological measurements, environmental conditions, and personal profile attributes within a unified semantic architecture. Unlike existing Internet of Things (IoT) ontologies such as SSN/SOSA, SAREF, SAREF4EHAW, and OntoDomus, which primarily emphasize interoperability, AALHealthIoT operationalizes cross-domain health-risk inference using Semantic Web Rule Language (SWRL) rules grounded in publicly available clinical guidelines. The ontology comprises 188 classes, 159 object properties, 30 data properties, and 2,077 axioms and was developed using the NeOn methodology. Validation included structural verification, pitfall detection, logical consistency checking, competency-question testing, and quantitative performance evaluation. Under controlled synthetic scenarios ranging from 50 to 1,000 seniors (up to 8,000 ontology individuals), SWRL reasoning remained under 2 seconds, while SPARQL Protocol and RDF Query Language (SPARQL) query latency remained below 1 second, even for the most complex cross-domain queries. These results demonstrate that ontology-driven semantic reasoning can provide an explainable and computationally feasible foundation for decision support in community-based AAL environments.
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