Affect detection from body language during social HRI

Abstract

In order for robots to effectively engage a person in bi-directional social human-robot interaction (HRI), they need to be able to perceive and respond appropriately to a person's affective state. It has been shown that body language is essential in effectively communicating human affect. In this paper, we present an automated real-time body language recognition and classification system, utilizing the Microsoft <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Kinect™ sensor, that determines a person's affect in terms of their accessibility (i.e., openness and rapport) towards a robot during natural one-on-one interactions. Social HRI experiments are presented with our human-like robot Brian 2.0 and a comparison study between our proposed system and one developed with the Kinect™ body pose estimation algorithm verifies the performance of our affect classification system in HRI scenarios.