A Semantic Framework to Detect Problems in Activities of Daily Living Monitored through Smart Home Sensors.
Sensors (Basel)
; 24(4)2024 Feb 08.
Article
em En
| MEDLINE
| ID: mdl-38400265
ABSTRACT
Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a semantic framework for detecting problems in ADLs execution, monitored through smart home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed framework combines multiple Semantic Web technologies (i.e., ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations, forming a knowledge graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviors in ADL execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people across the dementia spectrum by offering a comprehensive way for clinicians to describe problematic behaviors in the everyday life of an individual.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Semântica
/
Atividades Cotidianas
Limite:
Humans
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Grécia