Predicting Extraversion from Non-verbal Features During a Face-to-Face Human-Robot Interaction

Abstract

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].

A Systematic Review of Robotic Rehabilitation for Cognitive Training

“…Similar to human-human interaction during cognitive training by human therapists, robots need to be able to interact with users naturally in robotic rehabilitation. This includes having a good understanding of user's emotions (e.g., happiness, shame, engagement), intentions and personality (Pettinati and Arkin, 2015 ; Rahbar et al, 2015 ; Vaufreydaz et al, 2016 ; Rudovic et al, 2018 ), being able to provide an emotional response when being shared with personal information (de Graaf et al, 2015 ; Chumkamon et al, 2016 ), talking day-by-day more to the user on various topics like hobbies, and dealing with novel events (Dragone et al, 2015 ; Kostavelis et al, 2015 ; Adam et al, 2016 ; Ozcana et al, 2016 ). These natural user-robot interactions require powerful perception, reasoning, acting and learning modules in robots, or in other words, cognitive and social-emotional capabilities.…”

First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis

“…Nguyen et al [79] extend [77] to predict the Big-Five traits in addition to hirability impressions, focusing on postures and gestures (extracted from a mixture of manual annotations and automated methods) as well as cooccurrence events. Rahbar et al [105] addressed extraversion recognition during HRI, taking into account the first thin slices of the interaction. Multimodal features extracted from depth images (e.g., motion and human-robot distance) are used to train a Logistic Regression Classifier.…”

Multimodal Human-Human-Robot Interactions (MHHRI) Dataset for Studying Personality and Engagement

“…Combining three feature categories yielded the best results as compared to using individual features alone, e.g., classification performance increased from 64.71% to 77.45% for extroversion. Individual and interpersonal features together were also found to be useful for predicting extroversion within the scope of HRI in [29] where Rahbar et al combined similar individual features with interpersonal features including synchrony, dominance and proxemics.…”