[en] Context awareness is a key element in human-robot interaction. Being able to recognize user activity improves robot decision making when facing ordinary situations in home-like environments, as well as robot overall performance. In robotics applications, context recognition is usually performed using time of day and three subsystems: localization, perception, and dialog. The proposal described in this paper adds to this approach a fifth item to classify user activities: an environmental recognition component. The Environment Recognition Component (ERC) described in this article uses convolutional neuronal networks to classify ordinary acoustic signals present in indoor environments. This information is used by a second element, the Context Recognition Component (CRC) that infers the user activity using propositional calculus. The empirical evaluation of the framework presents an 86% of accuracy at ERC level, and the CRC inference system provides three times more contexts than the approach without ERC.
Disciplines :
Computer science
Author, co-author :
Rodriguez Lera, Francisco Javier ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Martín Rico, Francisco; Universidad Rey Juan Carlos
Matellán Olivera, Vicente; University of León
External co-authors :
yes
Title :
Context Awareness in shared human-robot Environments: Benefits of Environment Acoustic Recognition for User Activity Classification
Publication date :
2017
Event name :
8th International Conference of Pattern Recognition Systems (ICPRS 2017)
Event place :
Madrid, Spain
Event date :
from 11 July 2017 to 13 July 2017
Audience :
International
Main work title :
8th International Conference of Pattern Recognition Systems (ICPRS 2017), Madrid (Spain), 11-13 July 2017