SOUK: social observation of human kinetics

Simulating human-centered pervasive systems requires accurate assumptions on the behavior of human groups. Recent models consider this behavior as a combination of both social and spatial factors. Yet, establishing accurate traces of human groups is difficult: current techniques capture either positions, or contacts, with a limited accuracy. In this paper we introduce a new technique to capture such behaviors. The interest of this approach lies in the unprecedented accuracy at which both positions and orientations of humans, even gathered in a crowd, are captured. From the mobility to the topological connectivity, the open-source framework we developed offers a layered approach that can be tailored, allowing to compare and reason about models and traces. We introduce a new trace of 50 individuals on which the validity and accuracy of this approach is demonstrated. To showcase the interest of our software pipeline, we compare it against the random waypoint model. Our fine-grained analyses, that take into account social interactions between users, show that the random waypoint model is not a reasonable approximation of any of the phenomena we observed.