Unsupervised Activity Discovery and Characterization for Sensor-Rich Environments

Abstract

This thesis presents an unsupervised method for discovering and analyzing the different kinds of activities in an active environment. Drawing from natural language processing, a novel representation of activities as bags of event n-grams is introduced, where the global structural information of activities using their local event statistics is analyzed. It is demonstrated how maximal cliques in an undirected edge-weighted graph of activities, can be used in an unsupervised manner, to discover the different activity-classes. Taking on some work done in computer networks and bio-informatics, it is shown how to characterize these discovered activity-classes from a wholestic as well as a by-parts view-point. A definition of anomalous activities is formulated along with a way to detect them based on the difference of an activity instance from each of the discovered activity-classes. Finally, an information theoretic method to explain the detected anomalies in a human-interpretable form is presented. Results over extensive data-sets, collected from multiple active environments are presented, to show the competence and generalizability of the proposed framework.