The Sociometer: A Wearable Device for Understanding Human Networks

In this paper, we describe the use of the sociometer, a wearable sensor package, for measuring face-to-face interactions between people. We develop methods for learning the structure and dynamics of human communication networks. Knowledge of how people interact is important in many disciplines, e.g. organizational behavior, social network analysis and knowledge management applications such as expert finding. At present researchers mainly have to rely on questionnaires, surveys or diaries in order to obtain data on physical interactions between people. In this paper, we show how noisy sensor measurements from the sociometer can be used to build computational models of group interactions. Using statistical pattern recognition techniques such as dynamic Bayesian network models we can automatically learn the underlying structure of the network and also analyze the dynamics of individual and group interactions. We present preliminary results on how we can learn the structure of face-to-face interactions within a group, detect when members are in face-to-face proximity and also when they are having a conversation. We also measure the duration and frequency of interactions between people and the participation level of each individual in a conversation.