Reference : Context Awareness in shared human-robot Environments: Benefits of Environment Acousti...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Context Awareness in shared human-robot Environments: Benefits of Environment Acoustic Recognition for User Activity Classification
Rodriguez Lera, Francisco Javier mailto [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]
8th International Conference of Pattern Recognition Systems (ICPRS 2017), Madrid (Spain), 11-13 July 2017
Institution of Engineering and Technology
24 (6 .)-24 (6 .)(1)
8th International Conference of Pattern Recognition Systems (ICPRS 2017)
from 11 July 2017 to 13 July 2017
[en] ERC;context awareness;convolutional neuronal networks;shared human-robot environments;human-robot interaction;acoustic recognition;environmental recognition component;ordinary acoustic signal classification;CRC;user activity classification;context recognition component;
[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.

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