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. Semantic technologies have emerged as an effective way to not only deal with the issue of interoperability associated with heterogeneous health sensor data, but also to represent expert health knowledge to support complex reasoning required for decision-making. This study evaluates the state of the art in the use of semantic technologies in sensor-based personal health monitoring systems. Using a systematic approach, a total of 40 systems representing the state of the art in the field are analysed. Through this analysis, six key challenges that such systems must overcome for optimal and effective health monitoring are identified: interoperability, context awareness, situation detection, situation prediction, decision support, and uncertainty handling. The study critically evaluates the extent to which these systems incorporate semantic technologies to deal with these challenges and identifies the prominent architectures, system development and evaluation methodologies that are used. The study provides a comprehensive mapping of the field, identifies inadequacies in the state of the art, and provides recommendations for future research directions.

翻译：近年来，早期检测、预防和预测疾病日益受到关注。这一趋势，加之传感器技术和物联网的进步，加速了个人健康监测系统的研发进程。语义技术不仅有效解决了与异构健康传感器数据相关的互操作性问题，还能表示专家健康知识以支持决策所需的复杂推理。本研究评估了语义技术在基于传感器的个人健康监测系统中的应用现状。通过系统化方法，共分析了40个代表该领域前沿水平的系统。分析揭示了这些系统为实现最优且有效的健康监测必须克服的六大关键挑战：互操作性、情境感知、态势检测、态势预测、决策支持及不确定性处理。本研究批判性地评估了这些系统在应对上述挑战时对语义技术的融合程度，并识别了所采用的主流架构、系统开发与评估方法论。该研究提供了该领域的全面映射，指出了当前技术的不足，并为未来研究方向提出了建议。