strength of probabilistic system based approaches compared to methods based on ontology reasoning is their ability to handle noise, uncertainty and incomplete in sensor data [@Chen2009].

Tapia [@Tapia2004] was the first to explore supervised learning methods to infer human activity from passive sensors, using a naive Bayes classifier. More recently, probabilistic graphical models started to play an important role in the activity recognition field [@Kasteren2008; @Kasteren2007]. Van Kasteren et al. [@Kasteren2008] explored the use Conditional Random Fields [@Lafferty2001] and Hidden Markov Models [@Rabiner1986]. Van Kasteren and Kr[ö]{}se [@Kasteren2007] applied Bayesian Networks [@Friedman1997] on the activity recognition task. Kim et al. [@Kim2010] found Hidden Markov Models to be incapable of capturing long-range or transitive dependencies between observations, which results in difficulties recognizing multiple interacting activities (concurrent or interwoven). Conditional Random Fields do not posses these limitations.

The main differences between existing work in activity recognition and the approach presented in this paper are the input data on which they can be applied and the generality of the approach. Activity recognition techniques consider the input data to be a multidimensional time series of the sensor values over time based on which time windows are mapped onto human activities. An appropriate time window 