Interpretation of Social Touch on an Artificial Arm Covered with an EIT-based Sensitive Skin

During social interaction humans extract important information from tactile stimuli that improves their understanding of the interaction. The development of a similar capacity in a robot will contribute to the future success of intuitive human-robot interactions. This paper presents experiments on the classification of social touch on a fullsized mannequin arm covered with touch-sensitive artificial skin. The flexible and stretchable sensitive skin was implemented using electrical impedance tomography. A classifier based on the LogitBoost algorithm was used to classify six emotions and six social messages transmitted by humans when touching the artificial arm. Experimental results show that classification of social touch can be achieved with accuracies comparable to those achieved by humans.