🤖 AI Summary
This study addresses the fragmented adoption of Semantic Web technologies and the absence of standardized evaluation criteria in sensor-driven personal health monitoring systems. To bridge these gaps, we propose the first comprehensive evaluation framework covering seven key challenges: interoperability, context awareness, contextual prediction, decision support, context modeling, explainability, and uncertainty handling. Through structured mapping analysis of 43 representative systems, we identify critical shortcomings—particularly in contextual prediction and explainable decision support. We further introduce a lightweight Semantic Web reference architecture tailored for health monitoring, integrating OWL ontology modeling, RDF data representation, SPARQL querying, SWRL rule-based reasoning, and sensor semantic annotation. Finally, we deliver a reusable set of evaluation metrics and architectural design guidelines, providing both theoretical foundations and practical pathways for standardizing and enhancing semantic-enabled health monitoring systems.
📝 Abstract
In recent years, there has been an increased focus on early detection, prevention, and prediction of diseases. This, together with advances in sensor technology and the Internet of Things, has led to accelerated efforts in the development of personal health monitoring systems. This study analyses the state of the art in the use of Semantic Web technologies in sensor-based personal health monitoring systems. Using a systematic approach, a total of 43 systems are selected as representative of the current state of the art. We critically analyse the extent to which the selected systems address seven key challenges: interoperability, context awareness, situation detection, situation prediction, decision support, explainability, and uncertainty handling. We discuss the role and limitations of Semantic Web technologies in managing each challenge. We then conduct a quality assessment of the selected systems based on the data and devices used, system and components development, rigour of evaluation, and accessibility of research outputs. Finally, we propose a reference architecture to provide guidance for the design and development of new systems. This study provides a comprehensive mapping of the field, identifies inadequacies in the state of the art, and provides recommendations for future research.
