Best Poster Award in the context of HCI International 2017, 9 - 14 July 2017, Vancouver, Canada

Certificate for Best Poster Extended Abstract Award
conferred to

Stephanie J. Smith, Bradly T. Stone (Advanced Brain Monitoring Inc., United States),
Thavidu Ranatunga (Fello Robots, United States), Kyle Nel (Lowe’s Innovation Lab., United States),
Thomas Z. Ramsoy (Neurons Inc., Denmark), and Chris Berka (Advanced Brain Monitoring Inc., United States)

for the poster entitled

"Neurophysiological Indices of Human Social Interactions between Humans and Robots"

Presented in the context of
HCI International 2017
9 - 14 July 2017, Vancouver, Canada

Poster Abstract
"Technology continues to advance at exponential rates and we are exposed to a multitude of electronic interfaces in almost every aspect of our lives. In order to achieve seamless integration of both, human and technology, we must examine the objective and subjective responses to such interactions. The goal of this study was to examine neurophysiological responses to movement, communication, and usability with a robot assistant, in comparison to human assistant, in a real-world setting. OSHbot (robot assistants designed by Fellow Robots) were utilized as mobile store clerks to identify and locate merchandise in order to assist customers in finding items within a hardware store. By acquiring neurophysiological measures (electroencephalogram; EEG and electrocardiogram; ECG) of human perception and interaction with robots, we found evidence of Mirror Neuron System (MNS) elicitation and motor imagery processing, which is consistent with other studies examining human-robot interactions. Multiple analyses were conducted to assess differences between human-human interaction and human-robot interaction. Several EEG metrics were identified that were distinguishable based on interaction type; among these was the change observed across the Mu bandwidth (8–13 Hz). The variance in this EEG correlate has been related to empathetic state change. In order to explore differences in the interactions related to gender and age additional analyses were conducted to compare the effects of human-human interaction versus human-robot interaction with data stratified by gender and age. This analysis yielded significant differences across these categories between human-human interaction and human-robot interaction within EEG metrics. These preliminary data show promise for future research in the field of human-robot relations in contributing to the design and implementation of machines that not only deliver basic services but also create a social connection with humans."

The full poster is available through SpringerLink, provided that you have proper access rights.