Design of Haptic Gestures for Affective Social Signaling Through a Cushion Interface

In computer-mediated communication, the amount of non-verbal cues or social signals that machines can support is still limited. By integrating haptic information into computational systems, it might be possible to give a new dimension to the way people convey social signals in mediated communication. This research aims to distinguish different haptic gestures using a physical interface with a cushion-like form designed as a mediator for remote communication scenarios. The proposed interface can sense the user through the cushion’s deformation data combined with motion data. The contribution of this paper is the following: 1) Regardless of each participant’s particular interpretation of the gesture, the proposed solution can detect eight haptic gestures with more than 80% of accuracy across participants, and 2) The classification of gestures was done without the need of calibration, and independent of the orientation of the cushion. These results represent one step toward the development of affect communication systems that can support haptic gesture classification.