Analyzing User Trajectories from Mobile Device Data with Hierarchical Dirichlet Processes
Mobile devices have become pervasive among users in both work environments as well as everyday life, and they sense a wealth of information that can be exploited for a variety of tasks, such as activity recognition, security or health monitoring. In this paper, we explore the feasibility of trajectory clustering, i.e., detecting similarities between moving objects, for an application related to workplace productivity improvement. We use Hierarchical Dirichlet Processes due to their ability to automatically extract appropriate trajectory segments. The application domain is the analysis of RSSI data, where this machine learning method proves successfully.
KeywordsReceive Signal Strength Indicator Dynamic Time Warping Anchor Node Dirichlet Process Trajectory Cluster
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