  Emotions have a major interactive role in defining how humans interact with
their environment by encoding their perception to external events and
accordingly, influencing their cognition and decision-making process.
Therefore, increasing attention has been directed toward integrating human
affective states into system design in order to optimize the quality of task
performance. In this work, we seize on the significant correlation between
emotions and behavioral performance that is reported in several psychological
studies and develop an online closed-loop design framework for Human-Robot
Interaction (HRI). The proposed approach monitors the behavioral performance
based on the levels of Pleasure, Arousal, and Dominance (PAD) states for the
human operator and when required, applies an external stimulus which is
selected to induce an improvement in performance. The framework is implemented
on an HRI task involving a human operator teleoperating an articulated robotic
manipulator. Our statistical analysis shows a significant decrease in pleasure,
arousal, and dominance states as the behavioral performance deteriorates $(p <
0.05)$. Our closed-loop experiment that uses an audio stimulus to improve
emotional state shows a significant improvement in the behavioral performance
of certain subjects.
