Detecting Situations with Stream Reasoning on Health Data Obtained with IoT

The development of Internet of Things (IoT) creates large amount of data usable by decision making systems in various domains. In particular, in the field of health monitoring, it enables to follow the medical state of a patient at home in real-time. A challenge is to interpret these data with a high-level representation model in order to have a better understanding of the medical state of a patient. We propose in this article to use Stream Reasoning associated to an ontological representation of the medical context of a patient to understand her situation. This permits to combine in real time static knowledge stored in an ontology and dynamic information provided by smart sensors. To facilitate this process, we introduce constraints and situations concepts to ease the translation of expert knowledge into logical queries. We provide in this paper an experimental analysis of real body temperature data to illustrate how situations may be detected.