Route Separation Strategies for Human Movement Datasets

Learning patterns of human movement is a complex and hard task, including several computationally expensive algorithms. This issue has even higher emphasis in mobile environment, since handheld devices contain significantly less memory and computing power than a usual PC does. In this paper we are going to compare novel, mobile-optimized methods for separating trajectories in a human routine recognition framework.