Joint segmentation and activity discovery using semantic and temporal priors

We introduce a hierarchical nonparametric topic modeling approach to infer activity routines from context sensor data streams based on a distance dependent Chinese restaurant process (ddCRP). Our approach does not require labeled data at any stage. Neither does our approach depend on time-invariant sliding windows to sample context word statistics. Our activity discovery approach builds on the idea that context words occurring within one activity are semantically similar, whereas context words of different activities are less similar. Context word streams are segmented into supersamples and then semantic and temporal features are obtained to construct a segmentation prior that relates supersamples via its context words. Our hierarchical model uses the segmentation prior and ddCRP to group supersamples and the Chinese restaurant process (CRP) to discover activities. We evaluate our approach using the Opportunity dataset that contains activities of daily living. Besides being nonparametric, our ddCRP based model outperforms both, classic parametric latent Dirichlet allocation (LDA) and the nonparametric Chinese restaurant franchise (CRF). We conclude that ddCRP+CRP is an adequate approach for fully unsupervised activity discovery from context sensor data.