Semantic web technologies in sensor-based personal health monitoring systems: A systematic mapping study

TL;DR

This systematic mapping study reviews how Semantic Web technologies are used in sensor-based personal health monitoring systems, analyzing 48 systems to identify challenges, limitations, and future directions.

Contribution

It provides a comprehensive analysis of the current state, limitations, and a reference architecture for designing health monitoring systems using Semantic Web technologies.

Findings

Semantic Web technologies address interoperability and context awareness challenges.

Many systems lack rigorous evaluation and accessibility of research outputs.

The study proposes a reference architecture for future system development.

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 48 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, situation detection, situation prediction, decision support, context awareness, 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.