Predicting extraversion from non-verbal features during a face-to-face human-robot interaction

Abstract : In this paper we present a system for automatic prediction of extraversion during the first thin slices of human-robot interaction (HRI). This work is based on the hypothesis that personality traits and attitude towards robot appear in the behavioural response of humans during HRI. We propose a set of four non-verbal movement features that characterize human behavior during interaction. We focus our study on predicting Extraversion using these features , extracted from a dataset consisting of 39 healthy adults interacting with the humanoid iCub. Our analysis shows that it is possible to predict to a good level (64%) the Extraversion of a human from a thin slice of interaction relying only on non-verbal movement features. Our results are comparable to the state-of-the-art obtained in HHI [ 23 ] .
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Communication dans un congrès
International Conference on Social Robotics, Oct 2015, Paris, France. pp.10
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Faezeh Rahbar, Salvatore Anzalone, Giovanna Varni, Elisabetta Zibetti, Serena Ivaldi, et al.. Predicting extraversion from non-verbal features during a face-to-face human-robot interaction. International Conference on Social Robotics, Oct 2015, Paris, France. pp.10. 〈hal-01189065〉

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