Reasoning Systems for AAL


Human activity recognition is a key enabler for Ambient Assisted Living and provides a paradigmatic example of how reasoning capacities can be used in such scenarios. In order to detect and recognise human activities, first of all, humans have to be monitored using sensors. The information grabbed by those sensors feed the modelling and inference layers, where the reasoning takes place. In this chapter, the most used inference and reasoning techniques have been introduced. Reasoning systems for AAL have been divided into three different categories, namely, the data-driven approach, the knowledge-driven approach, and the hybrid approach. The most representative examples of those approaches have been presented, describing the advantages and disadvantages. Due to the emergence of the Semantic Web and its application to AAL scenarios, has been devoted to describe the features of OWL and associated semantic reasoners, which can be classified as a knowledge-driven approach.