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A novel affect-sensitive human-robot cooperative framework is presented in this paper. Peripheral physiological indices are measured through wearable biofeedback sensors to detect the affective state of the human. Affect recognition is performed through both quantitative and qualitative analyses. A subsumption control architecture sensitive to the affective state of the human is proposed for a mobile robot. Human-robot cooperation experiments are performed where the robot senses the affective state of the human and responds appropriately. The results presented here validate the proposed framework and demonstrate a new way of achieving implicit communication between a human and a robot.

Robots are expected to be pervasive in the society in a not too distant future where they will work extensively as assistants of humans in various activities. With this in view, a novel affect-sensitive architecture for human-robot cooperation is presented in this paper where the robot is expected to recognize human psychological states. As a demonstration, an online heart rate variability analysis to infer the mental stress of a human engaged in a task is presented. This technique involves real-time heart rate monitoring, signal processing using both Fourier Transforrn and Wavelet Transform, and inferring the stress condition based on the level of activation of the sympathetic and parasympathetic nervous systems using fuzzy logic. Results from human subject trials are presented to validate the presented methodology. This stress detection technique is expected to be useful in the future human-robot cooperation activities, where the robot will recognize human stress and respond appropriately.

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