Recently, a number of ontology-driven healthcare systems have been leveraged by the Internet-of-Things (IoT) technologies, which offer opportunities to improve patient monitoring and abnormal situation detection with support of medical wearables and cloud infrastructure. Usually, these systems rely on IoT ontologies to represent sensor data observations. The ETSI Smart Appliances REFerence (SAREF) IoT ontology is an extensible industry-oriented standard. In this paper, we discuss the verbosity problem of SAREF when used for real-time electrocardiography (ECG), emphasizing the requirement of representing time series. We compared the main ontologies in this context according to quality, message size (payload), IoT-orientation and standardization. We also introduce a SAREF4health extension to tackle the verbosity problem. In the SAREF4health development we followed ontology-driven conceptual modelling, in which an ECG ontology grounded in the Unified Foundational Ontology (UFO) plays the role of a reference model. The methodology was enhanced by a standardization procedure and considers the RDF serialization of the HL7 Fast Healthcare Interoperability Resources (FHIR) standard. The validation of SAREF4health includes the use cases of an early warning system that uses ECG data to detect accidents with truck drivers in a port area. A prototype that integrates an existing ECG wearable with cloud infrastructure demonstrates the performance impact of SAREF4health considering IoT constraints. Our results show that SAREF4health is adequate to enable semantic interoperability of IoT solutions that need to deal with frequency-based time series. Design decisions regarding the trade-off between ontology quality and aggregation representation are also discussed.
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