  During social interactions, people use auditory, visual, and haptic cues to
convey their thoughts, emotions, and intentions. Due to weight, energy, and
other hardware constraints, it is difficult to create devices that completely
capture the complexity of human touch. Here we explore whether a sparse
representation of human touch is sufficient to convey social touch signals. To
test this we collected a dataset of social touch interactions using a soft
wearable pressure sensor array, developed an algorithm to map recorded data to
an array of actuators, then applied our algorithm to create signals that drive
an array of normal indentation actuators placed on the arm. Using this
wearable, low-resolution, low-force device, we find that users are able to
distinguish the intended social meaning, and compare performance to results
based on direct human touch. As online communication becomes more prevalent,
such systems to convey haptic signals could allow for improved distant
socializing and empathetic remote human-human interaction.
