Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS)
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...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8543567/ |
| _version_ | 1819276182964666368 |
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| author | Luz Santamaria-Granados Mario Munoz-Organero Gustavo Ramirez-Gonzalez Enas Abdulhay N. Arunkumar |
| author_facet | Luz Santamaria-Granados Mario Munoz-Organero Gustavo Ramirez-Gonzalez Enas Abdulhay N. Arunkumar |
| author_sort | Luz Santamaria-Granados |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T23:36:10Z |
| format | Article |
| id | doaj.art-1d8d0d1de8b143778f93ae5ad0085804 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:36:10Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1d8d0d1de8b143778f93ae5ad00858042022-12-21T17:25:51ZengIEEEIEEE Access2169-35362019-01-017576710.1109/ACCESS.2018.28832138543567Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS)Luz Santamaria-Granados0Mario Munoz-Organero1Gustavo Ramirez-Gonzalez2https://orcid.org/0000-0002-1338-8820Enas Abdulhay3N. Arunkumar4Faculty of Systems Engineering, Universidad Santo Tomás, Tunja, ColombiaTelematics Engineering Department, UC3M-BS Institute of Financial Big Data, Universidad Carlos III de Madrid, Leganes, SpainTelematics Department, University of Cauca, Popayán, ColombiaDepartment of Biomedical Engineering, Faculty of Engineering, Jordan University of Science and Technology, Irbid, JordanDepartment of Electronics and Instrumentation, SASTRA University, Thanjavur, IndiaRecommender 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.https://ieeexplore.ieee.org/document/8543567/Emotion recognitiondeep convolutional neural networkphysiological signalsmachine learningAMIGOS dataset |
| spellingShingle | Luz Santamaria-Granados Mario Munoz-Organero Gustavo Ramirez-Gonzalez Enas Abdulhay N. Arunkumar Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS) IEEE Access Emotion recognition deep convolutional neural network physiological signals machine learning AMIGOS dataset |
| title | Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS) |
| title_full | Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS) |
| title_fullStr | Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS) |
| title_full_unstemmed | Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS) |
| title_short | Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS) |
| title_sort | using deep convolutional neural network for emotion detection on a physiological signals dataset amigos |
| topic | Emotion recognition deep convolutional neural network physiological signals machine learning AMIGOS dataset |
| url | https://ieeexplore.ieee.org/document/8543567/ |
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