Home // International Journal On Advances in Life Sciences, volume 13, numbers 1 and 2, 2021 // View article
Authors:
Lampros Mpaltadoros
Vasilis Alepopoulos
Antonios Pliatsios
Angelos V. Vasileiadis
Thanos G. Stavropoulos
Spiros Nikolopoulos
Ioannis Kompatsiaris
Keywords: Ontology; Event Detection; Semantic Web; SPIN; Reasoning; Rule-Based Systems; Dementia.
Abstract:
In this paper we describe a rule-based framework for the detection of Health-related Problems of people with dementia, developed in the support2LIVE project. The framework combines a novel ontology for lifestyle data (steps, sleep duration and heart rate measurements) and Health-related Problem representation, a framework for IoT data collection from sensors, a Knowledge Ingestion component that links the data through meaningful relationships and a novel set of SPARQL Inferencing Notation (SPIN) Rules to infer problems from these data. Both the ontology and the rule set are designed based on clinical expert knowledge in the field of dementia. More specifically, lifestyle data is acquired from wearable devices available in the market, making the system affordable and convenient. A model based on Semantic Web technology, OWL (Web Ontology Language), is used to formally represent and integrate sensor measurements, promoting interoperability with other models and data exchange. SPIN rules offer the benefit of simplicity and flexibility as opposed to other rule representations in the domain. Our framework handles the incoming IoT data from the sensors with the use of suitable APIs and contains a Knowledge Ingestion component that transforms the received data to semantic knowledge in the form of RDFs. Finally, a clinician dashboard visualizes results to allow decision making, as shown in proof-of-concept scenarios from real participants in the support2LIVE pilots.
Pages: 32 to 41
Copyright: Copyright (c) to authors, 2021. Used with permission.
Publication date: December 31, 2021
Published in: journal
ISSN: 1942-2660