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The study of group dynamics is of primary importance in psychology and medicine because group dynamics affects the state of each member in a group. We study the RR interval time series, a heart rate measure, for a group of individuals involved in Kundalini meditation sessions. We analyze individual signals and study the wavelet coherence among the heart rate variability of different individuals. For specific activities, we found a high degree of coherence among all the people in the group. We also propose a novel method to detect temporally varying connections among the individuals based on the coherence of their heart rate.

The use of wireless technology and advanced signal processing for personalized healthcare is extremely promising. A relevant application appears to be the field of group dynamics, in which the study of a heart-rate measure, the RR interval time series, can reveal valuable information about individuals, as well as the group as a whole. In this paper, we present the results of computing the wavelet coherence among the RR interval time series of a group of people during a Kundalini yoga meditation session, revealing entrainment among them during specific activities and additional important information, such as patterns of heart rate variability that are common to all members of the group. We propose a novel method to study the connections among people in real time, underlying the bounds in the accuracy as a function of the delay introduced.

In this paper, we present an application of probabilistic graphical models such as Bayesian Networks (BNs) for capturing the spatio-temporal factors in cognitive networks. We propose to use a BN that makes use of historical network information to learn the network behavior across spatio-temporalspectral dimensions and predicts best configuration for each Access Point (AP) in a Wireless LAN (WLAN) system. We further present the application of BNs for traffic prediction as well as channel selection in a cognitive WLAN scenario. Our results prove that the space and time are critical factors that can impact the performance of the network configuration. We noticed improvement in traffic prediction accuracy and channel selection accuracy, respectively, of 35% and 40%, when using space and time information.