Describing user activity plays an essential role on Ambient Intelligence. In this work, we review different methods for human activity recognition, classified as data-driven and knowledge-based techniques. We focus in context ontologies whose ultimate goal is the tracking of human behaviour. After studying upper and domain ontologies, both useful for human activity representation and inference, we establish an evaluation criterion to assess the suitability of the different candidate ontologies for this purpose. As a result, any missing features, which are relevant for modelling daily human behaviours, are identified as future challenges.