Different Strokes and Different Folks: Economical Dynamic Surface Sensing and Affect-Related Touch Recognition

Social touch is an essential non-verbal channel whose great interactive potential can be realized by the ability to recognize gestures performed on inviting surfaces. To assess impact on recognition performance of sensor motion, substrate and coverings, we collected gesture data from a low-cost multitouch fabric pressure-location sensor while varying these factors. For six gestures most relevant in a haptic social robot context plus a no-touch control, we conducted two studies, with the sensor (1) stationary, varying substrate and cover (n=10); and (2) attached to a robot under a fur covering, flexing or stationary (n=16). For a stationary sensor, a random forest model achieved 90.0% recognition accuracy (chance 14.2%) when trained on all data, but as high as 94.6% (mean 89.1%) when trained on the same individual. A curved, flexing surface achieved 79.4% overall but averaged 85.7% when trained and tested on the same individual. These results suggest that under realistic conditions, recognition with this type of flexible sensor is sufficient for many applications of interactive social touch. We further found evidence that users exhibit an idiosyncratic `touch signature', with potential to identify the toucher. Both findings enable varied contexts of affective or functional touch communication, from physically interactive robots to any touch-sensitive object.