References of "Matellán, Vicente"
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See detailBenchmark Dataset for Evaluation of Range-Based People Tracker Classifiers in Mobile Robots
Álvarez-Aparicio, Claudia; Guerrero-Higueras, Ángel Manuel; Olivera, Maria Carmen Calvo et al

in Frontiers in Neurorobotics (2018), 11

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See detailEmpirical analysis of cyber-attacks to an indoor real time localization system for autonomous robots
Guerrero-Higueras, Ángel Manuel; DeCastro-García, Noemí; Rodriguez Lera, Francisco Javier UL et al

in Computers and Security (2017), 70(Supplement C), 422-435

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See detailDeep Learning and Bayesian Networks for Labelling User Activity Context Through Acoustic Signals
Rodriguez Lera, Francisco Javier UL; Rico, Francisco Martín; Matellán, Vicente

in Ferrández Vicente, José Manuel; Álvarez-Sánchez, José Ramón; de la Paz López, Félix (Eds.) et al Biomedical Applications Based on Natural and Artificial Computing: International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Corunna, Spain, June 19-23, 2017, Proceedings, Part II (2017)

Context awareness in autonomous robots is usually performed combining localization information, objects identification, human interaction and time of the day. We think that gathering environmental sounds ... [more ▼]

Context awareness in autonomous robots is usually performed combining localization information, objects identification, human interaction and time of the day. We think that gathering environmental sounds we can improve context recognition. With that purpose, we have designed, developed and tested an Environment Recognition Component (ERC) that provides an extra input to our Context-Awareness Component (CAC) and increases the rate of labeling correctly users' activities. First element, the Environment Recognition Component (ERC) uses convolutional neural networks to classify acoustic signals and providing information to the Context-Awareness Component (CAC) which infers the user activity using a hierarchical Bayesian network. The work described in this paper evaluates the results of the labeling process in two HRI scenarios: robot and user sharing room and robot, and when the human and the robot are in different rooms. The results showed better accuracy when the ERC uses acoustic signals. [less ▲]

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