ADR-SPLDA: Activity discovery and recognition by combining sequential patterns and latent Dirichlet allocation

This paper presents ADR-SPLDA, an unsupervised model for human activity discovery and recognition in pervasive environments. The activities are encoded in sequences recorded by non-intrusive sensors placed at various locations in the environment. Our model studies the relationship between the activities and the sequential patterns extracted from the sequences. Activity discovery is formulated as an optimization problem in which sequences are modeled as probability distributions over activities, and activities are, in turn, modeled as probability distributions over sequential patterns. The optimization problem is solved by maximization of the likelihood of data. We present experimental results on real datasets gathered in smart homes where people perform various activities of daily living. The results obtained demonstrate the suitability of our model for activity discovery and characterization. Also, we empirically demonstrate the effectiveness of our model for activity recognition by comparing it with two of the widely used models reported in the literature, the Hidden Markov model and the Conditional Random Field model.