## 2 Related Work  
There are two main monitoring approaches for automatic human behaviour and activity evaluation, namely, vision- and sensor-based monitoring. For a review of vision-based approaches, [10] can be consulted. When approaching human behaviour and activity evaluation recognition in intelligent environments, sensor-based behaviour and activity evaluation are the most widely used solutions [11], as vision-based approaches tend to generate privacy concerns among the users [12]. Sensor-based approaches are based on the use of emerging sensor network technologies for behaviour and activity monitoring. The generated sensor data from sensor-based monitoring are mainly time series of state changes and/or various parameter values that are usually processed through data fusion, probabilistic or statistical analysis methods and formal knowledge technologies for activity recognition. There are two main approaches for sensor-based behaviour and activity recognition in the literature: data- and knowledge-driven approaches. The idea behind data-driven approaches is to use data mining and machine learning techniques to learn behaviour and activity models. These are usually presented as a supervised learning approach, for which different techniques have been used to learn behaviours and activities from collected sensor data. Data-driven approaches need big datasets of labelled activities to train different kinds of classifiers. The learning techniques used in the literature are broad, from simple naive Bayes classifiers [13] to hidden Markov models [14], dynamic Bayesian networks [15], support vector machines [16] and online (or incremental) classifiers [17]. The knowledge-driven approaches try to use the existing domain knowledge in order to avoid using labelled datasets for the training. [PERSON] and [PERSON] [18] tried to overcome the problem of depending on manually labelled activity datasets by extracting activity clusters using unsupervised learning techniques. These clusters are used to train a boosted hidden Markov model, which is shown to be able to recognise several activities. [PERSON] et al. [19] used logic-based approaches for activity recognition, on the basis of previous knowledge from the domain experts. Others have adopted ontology-based approaches, which allow for a commonly agreed explicit representation of activity definitions independent of algorithmic choices, thus facilitating portability, interoperability and reusability [20; 21; 22]. User behaviour in intelligent environments builds on user activities to describe the conduct of the user. Modelling user behaviour entails an abstraction layer over activity recognition. Behaviour models describe how specific users perform activities and what activities comprise their daily living. User behaviour prediction is an important task in intelligent environments. It allows us to anticipate user needs and to detect variations in behaviour that can be related to health risks. In the Mavhome project [23], the authors created algorithms to predict the users' mobility patterns and their device usage. Their algorithms, based mainly on sequence matching, compression and Markov models [24], allowed the intelligent environments to adapt to the user needs. Other authors have used prediction methods to recognize the user activities in smart environments [16]. An analysis of the importance of prediction in intelligent environments can be found in [25]. Prediction has also been used in intelligent environments for the control of artificial illumination [26] using neuro-fuzzy systems or for the control of climate parameters on the basis of user behaviour [27]. A more in-depth analysis of the use of user behaviour prediction for comfort management in intelligent environments can be found in [28]. As explained in the following section, we identify two types of behaviours: intra-activity behaviour (which describes how the user performs activities) and inter-activity behaviour (which describes the actions and activity sequencesthat compose the user's daily life).