Quantifying the inherent coordination between interacting individuals can afford us new insights into their emotions, communicative intent, and relationship quality. We propose a novel framework to capture the physiological synchrony between romantic partners through sparse representation techniques and appropriately designed parametric dictionaries that take into account the characteristic structure of the considered signals. Physiological synchrony is operationalized as the similarity of co-occurring electrodermal activity (EDA) streams captured through the distance in the corresponding parametric representation space, as well as through the joint signal representation errors. Results indicate that the proposed sparse EDA synchrony measures (SESM)-evaluated on two datasets of couples' interactions-differ across tasks of various emotional intensity and are associated with the partners' attachment style. These results provide a foundation towards designing novel descriptors of interaction and physiological linkage between individuals for emerging affective computing applications.
