Understanding the social dynamics of a group of people can give new insights into social behavior. Physical proximity between individuals results from the interactions between them. Hence, measuring physical proximity is an important step towards a better understanding of social behavior. We discuss a novel approach to sense proximity from within the social dynamics. Our primary objective is to construct a spatio-temporal social graph from noisy proximity data. We address the technical and algorithmic challenges of measuring proximity reliably and accurately. Simulations and real world experiments demonstrate the feasibility and scalability of our approach. Our algorithms doubles the sensitivity of proximity detections at the cost of a slight reduction in specificity.
|Number of pages||10|
|Publication status||Published - 1 Jan 2014|
|Event||2014 12th IEEE International Conference on Pervasive Computing and Communications, PerCom 2014 - Budapest, Hungary|
Duration: 24 Mar 2014 → 28 Mar 2014
Conference number: 12
|Conference||2014 12th IEEE International Conference on Pervasive Computing and Communications, PerCom 2014|
|Period||24/03/14 → 28/03/14|