  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.
