Affective State Recognition and Adaptation in Human-Robot Interaction: A Design Approach

Abstract

It is argued that a robotic system that is capable of implicit communication with a human and can modify its behavior appropriately based on such communication could be useful. This paper presents a closed loop human-robot interaction framework where the robot can recognize the affective state of the human implicitly and adapt to it appropriately. Affective cues are inferred by a robot in real-time using psychophysiological analysis where the physiological signals are measured through wearable biofeedback sensors. A robot-based basketball game is designed where a robotic "coach" monitors the participant's anxiety to alter the difficulty level of the game in a real-time closed loop manner. The results are compared with situations when anxiety is not monitored and the game is adapted only according to the performance. Results show that monitoring and responding to affective cues led to higher performance improvement of the participants under lower anxiety. This is the first time, to our knowledge, that the impact of such implicit communication between a robot and a human has been demonstrated experimentally