Wireless sensors can measure important physiological signals without interfering with everyday life. Among them, the heart rate is easy to measure and can
reveal valuable information about health and disease conditions. It is informative on the state of a single individual and surprisingly also on the
dynamics of a group of people. First, we analyze the heart rate signal using a Bayesian method, inferring in real time its probabilistic distribution and obtaining interesting insights into the influences of the autonomic nervous system. Then, using a wavelet coherence method, we analyze the signals from a group of people and detect group dynamics, i.e., processes that can dramatically influence the
outcomes of group activities, as well as the well-being of individuals in the group.