Data from: A reduced-dimensionality approach to uncovering dyadic modes of body motion in conversations

Face-to-face conversations are central to human communication and a
fascinating example of joint action. Beyond verbal content, one of the
primary ways in which information is conveyed in conversations is body
language. Body motion in natural conversations has been difficult to study
precisely due to the large number of coordinates at play. There is need
for fresh approaches to analyze and understand the data, in order to ask
whether dyads show basic building blocks of coupled motion. Here we
present a method for analyzing body motion during joint action using
depth-sensing cameras, and use it to analyze a sample of scientific
conversations. Our method consists of three steps: defining modes of body
motion of individual participants, defining dyadic modes made of
combinations of these individual modes, and lastly defining motion motifs
as dyadic modes that occur significantly more often than expected given
the single-person motion statistics. As a proof-of-concept, we analyze the
motion of 12 dyads of scientists measured using two Microsoft Kinect
cameras. In our sample, we find that out of many possible modes, only two
were motion motifs: synchronized parallel torso motion in which the
participants swayed from side to side in sync, and still segments where
neither person moved. We find evidence of dyad individuality in the use of
motion modes. For a randomly selected subset of 5 dyads, this
individuality was maintained for at least 6 months. The present approach
to simplify complex motion data and to define motion motifs may be used to
understand other joint tasks and interactions. The analysis tools
developed here and the motion dataset are publicly available.