Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS)

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dc.contributor.author Santamaria Granados, Luz
dc.contributor.author Muñoz Organero, Mario
dc.contributor.author Ramirez Gonzalez, Gustavo
dc.contributor.author Abdulhay, Enas
dc.contributor.author Arunkumar, N.
dc.date.accessioned 2021-01-27T13:47:52Z
dc.date.available 2021-01-27T13:47:52Z
dc.date.issued 2019-01-01
dc.identifier.bibliographicCitation L. Santamaria-Granados, M. Munoz-Organero, G. Ramirez-González, E. Abdulhay and N. Arunkumar, "Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS)," in IEEE Access, vol. 7, pp. 57-67, 2019
dc.identifier.issn 2169-3536
dc.identifier.uri http://hdl.handle.net/10016/31799
dc.description.abstract Recommender systems have been based on context and content, and now the technological challenge of making personalized recommendations based on the user emotional state arises through physiological signals that are obtained from devices or sensors. This paper applies the deep learning approach using a deep convolutional neural network on a dataset of physiological signals (electrocardiogram and galvanic skin response), in this case, the AMIGOS dataset. The detection of emotions is done by correlating these physiological signals with the data of arousal and valence of this dataset, to classify the affective state of a person. In addition, an application for emotion recognition based on classic machine learning algorithms is proposed to extract the features of physiological signals in the domain of time, frequency, and non-linear. This application uses a convolutional neural network for the automatic feature extraction of the physiological signals, and through fully connected network layers, the emotion prediction is made. The experimental results on the AMIGOS dataset show that the method proposed in this paper achieves a better precision of the classification of the emotional states, in comparison with the originally obtained by the authors of this dataset.
dc.description.sponsorship This research project is financed by theGovernment of Colombia, Colciencias and the Governorateof Boyacá
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2018 IEEE
dc.subject.other Emotion recognition
dc.subject.other Deep convolutional neural network
dc.subject.other Physiological signals
dc.subject.other Machine learning
dc.subject.other Amigos dataset
dc.subject.other Recognition
dc.subject.other Database
dc.subject.other Models
dc.title Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS)
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi 10.1109/ACCESS.2018.2883213
dc.rights.accessRights openAccess
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 57
dc.identifier.publicationlastpage 67
dc.identifier.publicationtitle IEEE Access
dc.identifier.publicationvolume 7
dc.identifier.uxxi AR/0000022920
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