Semantic Sensor Networks for Personalized Health Systems for Risk Prevention

Current monitoring systems for chronic disease management and assisted living are advancing with giant strides, providing more complete and personalized solutions. So far, standardization and physiological tracing strategies have mostly overcome difficulties dealing with integration and interoperability. However, with the deployment of massive sensing infrastructures, another big problem appears on the scene: an enormous amount of data, coming from the different and heterogeneous sources, and trying to describe one single scenario or situation. This problem becomes more and more evident in we focus in the needs of risk prevention scenarios, where the level of complexity of the targeted indicators relays in informal and less reliable sources. This paper proposes a new architecture for data indexing and correlation that provides a semantic middleware to search and select relevant information from a complex and flexible monitoring scenario in a work environment. Risk prevention must look back for trends and patterns, and furthermore, with a personalized approach. Indexing of semantic concepts would optimize algorithms to trigger emergency situations, provide dynamic and adaptive decision support and improving lifestyle and care of both employees and patients.