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Psychophysiological Feedback for Adaptive Human–Robot Interaction (HRI)

Chapter
Part of the Human–Computer Interaction Series book series (HCIS)

Abstract

Recent advances in robotics and sensing have given rise to a diverse set of robots and their applications. In recent years robots have increasingly applied in the service industry, search and rescue operations and therapeutic applications. The introduction of robots to interact with humans resulted in a dedicated field called human–robot interaction (HRI). Social HRI is of particular importance as it is the main focus of this chapter. This chapter presents an affect-inspired approach for social HRI. Physiological processing together with machine learning was employed to model affective states for an adaptive social HRI and its application in social interaction in the context of autism therapy was investigated.

Keywords

Autism Spectrum Disorder Affective State Emotion Recognition Galvanic Skin Response Pulse Transit Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported in part by a Marino Autism Research Institute (MARI) grant, an Autism Speaks Foundation Pilot grant, the National Science Foundation Grant [award number 0967170], and the National Institute of Health Grant [award number 1R01MH091102-01A1]. We would like to thank all colleagues that helped in this research and give special thanks to all subjects and their families.

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Vanderbilt UniversityNashvilleUSA

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