Social Impressions of the NAO Robot and its Impact on Physiology

TL;DR

This study investigates how different voices and motions of the NAO robot influence human perceptions and physiological responses, using ANOVA and deep learning classification methods.

Contribution

It explores the impact of robot modalities on social perceptions and introduces deep learning techniques to classify physiological responses.

Findings

Different robot modalities affect perceived safety and likability.

Deep learning models achieved over 25% accuracy in classifying physiological responses.

Prior robot experience influences social perceptions.

Abstract

The social applications of robots possess intrinsic challenges with respect to social paradigms and heterogeneity of different groups. These challenges can be in the form of social acceptability, anthropomorphism, likeability, past experiences with robots etc. In this paper, we have considered a group of neurotypical adults to describe how different voices and motion types of the NAO robot can have effect on the perceived safety, anthropomorphism, likeability, animacy, and perceived intelligence of the robot. In addition, prior robot experience has also been taken into consideration to perform this analysis using a one-way Analysis of Variance (ANOVA). Further, we also demonstrate that these different modalities instigate different physiological responses in the person. This classification has been done using two different deep learning approaches, 1) Convolutional Neural Network (CNN), and 2) Gramian Angular Fields on the Blood Volume Pulse (BVP) data recorded. Both of these approaches achieve better than chance accuracy 25% for a 4 class classification.