Modeling of interactions in a mobile context
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. We developed two complementary solutions to capture human traces, enabling both coarse-grained and fine-grained analyses. For coarse-grain and large-scale traces, we developed an application for smartphones to log every spatial and interaction events reported during an experiment [KRT10]. For fine-grain and small-scale traces, we introduced SOUK (Social Observation of hUman Kinetics) [KRTZ13], a new technique to capture crowd behaviors. The interest of SOUK lies in the unprecedented accuracy at which both positions and orientations of humans, even gathered in a crowd, are captured. We also developed an open-source framework to analyze such behavioral traces, offering a layered approach that can be tailored, allowing comparison between and reasoning about models and traces.
[KRTZ13] M-O. Killijian, M. Roy, G. Trédan, C. Zanon. “SOUK: Social Observation of Human Kinetics”. Proc. ACM joint conference on Ubiquitous and Pervasive Computing (Ubicomp 2013).
[KRT10] M-O. Killijian, M. Roy, G. Trédan: Beyond San Francisco Cabs: Building a *-lity Mining Dataset, Proc. Workshop on the Analysis of Mobile Phone Networks (NetMob2010), satellite of NetSci2010