Affect detection from body language during social HRI

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
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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.