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Are You Talking to Me? Detecting Attention in First-Person Interactions

Authors:
Luis Carlos González-García
Luz Abril Torres-Méndez
Julieta Martínez
Junaed Sattar
James J. Little

Keywords: Human-robot interaction; Body pose classification; Least squares approximations; Raw range data analysis

Abstract:
This paper presents an approach for a mobile robot to detect the level of attention of a human in first-person interactions. Determining the degree of attention is an essential task in day-to-day interactions. In particular, we are interested in natural Human-Robot Interactions (HRI’s) during which a robot needs to estimate the focus and the degree of the user’s attention to determine the most appropriate moment to initiate, continue and terminate an interaction. Our approach is novel in that it uses a linear regression technique to classify raw depth image data according to three levels of user attention on the robot (null, partial and total). This is achieved by measuring the linear independence of the input range data with respect to a dataset of user poses. We overcome the problem of time overhead that a large database can add to real-time Linear Regression Classification (LRC) methods by including only the feature vectors with the most relevant information. We demonstrate the approach by presenting experimental data from human interaction studies with a PR2 robot. Results demonstrate our attention classifier to be accurate and robust in detecting the attention levels of human participants.

Pages: 137 to 142

Copyright: Copyright (c) IARIA, 2015

Publication date: March 22, 2015

Published in: conference

ISSN: 2308-4197

ISBN: 978-1-61208-390-2

Location: Nice, France

Dates: from March 22, 2015 to March 27, 2015