A Semantic Framework to Detect Problems in Activities of Daily Living Monitored through Smart Home Sensors

• DOI: 10.3390/s24041107
• Publication Date: 2024-02-08T08:36:17Z
• Authors: Giorgos Giannios; Lampros Mpaltadoros; Vasilis Alepopoulos; Margarita Grammat...; Thanos G. Stavrop...; Spiros Nikolopoulos; Ioulietta Lazarou; Magda Tsolaki; Ioannis Kompatsiaris
• Keywords: Home Automation; Ambient Intelligence; Everyday Life; Smart environment

Activities of daily living (ADLs) are fundamental routine tasks that the majority 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. context work, conducted pilot study, gathering raw data from various sensors devices installed environment. The proposed combines multiple Semantic Web technologies (i.e., ontology, RDF, triplestore) to handle transform these into meaningful representations, forming knowledge graph. Subsequently, SPARQL queries used define construct explicit rules detect problematic behaviors ADL procedure leads generating new implicit knowledge. Finally, all available results visualized clinician dashboard. monitor deterioration performance across dementia spectrum by offering comprehensive way clinicians describe everyday life an individual.