Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Giorgos Giannios
ORCID logo ,
Lampros Mpaltadoros
ORCID logo ,
Vasilis Alepopoulos
,
Margarita Grammatikopoulou
,
Thanos G. Stavropoulos
ORCID logo ,
Spiros Nikolopoulos
* ORCID logo ,
Ioulietta Lazarou
,
Magda Tsolaki
ORCID logo ,
Ioannis Kompatsiaris
ORCID logo
Version 1 : Received: 18 December 2023 / Approved: 18 December 2023 / Online: 18 December 2023 (11:04:50 CET)

A peer-reviewed article of this Preprint also exists.

Giannios, G.; Mpaltadoros, L.; Alepopoulos, V.; Grammatikopoulou, M.; Stavropoulos, T.G.; Nikolopoulos, S.; Lazarou, I.; Tsolaki, M.; Kompatsiaris, I. A Semantic Framework to Detect Problems in Activities of Daily Living Monitored through Smart Home Sensors. Sensors 2024, 24, 1107. Giannios, G.; Mpaltadoros, L.; Alepopoulos, V.; Grammatikopoulou, M.; Stavropoulos, T.G.; Nikolopoulos, S.; Lazarou, I.; Tsolaki, M.; Kompatsiaris, I. A Semantic Framework to Detect Problems in Activities of Daily Living Monitored through Smart Home Sensors. Sensors 2024, 24, 1107.

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 behaviours in ADLs 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 in the spectrum of dementia, by offering a comprehensive way for clinicians to describe problematic behaviors in the every-day life of an individual.

Keywords

Smart Home; Sensors; ADLs; Semantic Web; Ontology; Knowledge Graph; SPARQL rules

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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