This paper presents a new unsupervised statistical model for human activity discovery and recognition in pervasive environments. The activities are encoded in sequences recorded by non-intrusive sensors disseminated in the environment. Our model studies the relationship between the activities and the sequential patterns from the sequence analysis perspective. Activity discovery is formulated as an optimization problem which is solved by maximization of the likelihood of data. We present experimental results on real datasets recorded in smart homes for persons performing their activities of daily living. The results obtained demonstrate the suitability of our model for activity discovery and recognition and how it outperforms most of the widely used approaches.
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