Unsupervised Embrace Pose Recognition using K-Means Clustering

Embrace is an essential part of human-social interactions. It also gives positive health benefits as much as other touch gestures. However, in the recent years, studies of embrace recognition has been largely ignored when compared to recognition of touch gestures such as pat or rub. There are different kinds of embrace, with humans and pets, which can express different meanings. As embraces can be as important as touches, we are interested in what kind of embraces we can model, specially for human-robot interaction. In this paper, we investigate an unsupervised embrace pose recognition system based on a soft-stuffed robot platform. Our proposed method includes a hardware implementation of soft fabric-based capacitive touch sensors and a software algorithm comprising of k-means clustering based on locational features extracted from a sliding window. The result shows that our proposed method can model embrace patterns as different clusters. The method is capable of recognizing and clustering unseen data to a similar cluster's patterns, though there is a limitation when modeling two poses whose touches are similar but different in the alignment of the robot. In the next step, we plan to improve and test the proposed method in real-time environment, and make adjustments to our sensing system to cope with found limitations. If successful, the proposed method will be integrated with a touch gesture recognizer into a gesture recognition system for creating interactive and affective responses with stuffed-toy robot, which can become a medium for robot therapy or our own pets at home.