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Authors:
Bruno Amaro
Vinicius Silva
Filomena Soares
João Sena Esteves
Keywords: Human-Robot Interaction; ZECA Robot; Distraction Patterns; Emotional States; Machine Learning.
Abstract:
The capacity of remaining focused on a task can be crucial in some circumstances. In general, this ability is intrinsic in a human social interaction and it is naturally used in any social context. Nevertheless, some individuals have difficulties in remaining concentrated in an activity, resulting in a short attention span. In order to recognize human distraction behaviours and capture the user attention, several patterns of distraction, as well as systems to automatically detect them, have been developed. One of the most used distraction patterns detection methods is based on the measurement of the head pose and eye gaze. The present work proposes a system based on a RGB camera, capable of detecting the distraction patterns, head pose, eye gaze, blinks frequency, and the distance of the user to the camera, during an activity, and then classify the user's state using a machine learning algorithm. The goal is to interface this system with a humanoid robot to consequently adapt its behaviour taking into account the individual affective state during an emotion imitation activity.
Pages: 152 to 157
Copyright: Copyright (c) IARIA, 2018
Publication date: September 16, 2018
Published in: conference
ISSN: 2308-3514
ISBN: 978-1-61208-660-6
Location: Venice, Italy
Dates: from September 16, 2018 to September 20, 2018