Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach
Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/5/1789 |
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| author | Chiara Filippini Adolfo Di Crosta Rocco Palumbo David Perpetuini Daniela Cardone Irene Ceccato Alberto Di Domenico Arcangelo Merla |
| author_facet | Chiara Filippini Adolfo Di Crosta Rocco Palumbo David Perpetuini Daniela Cardone Irene Ceccato Alberto Di Domenico Arcangelo Merla |
| author_sort | Chiara Filippini |
| collection | DOAJ |
| description | Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while extremely desirable, an accurate and automated emotion classification approach remains a challenging issue. To this end, this study presents an automated emotion recognition model based on easily accessible physiological signals and deep learning (DL) approaches. As a DL algorithm, a Feedforward Neural Network was employed in this study. The network outcome was further compared with canonical machine learning algorithms such as random forest (RF). The developed DL model relied on the combined use of wearables and contactless technologies, such as thermal infrared imaging. Such a model is able to classify the emotional state into four classes, derived from the linear combination of valence and arousal (referring to the circumplex model of affect’s four-quadrant structure) with an overall accuracy of 70% outperforming the 66% accuracy reached by the RF model. Considering the ecological and agile nature of the technique used the proposed model could lead to innovative applications in the affective computing field. |
| first_indexed | 2024-03-09T20:21:56Z |
| format | Article |
| id | doaj.art-00fb0b6ce2984253b207038ae0df9e9a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T20:21:56Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-00fb0b6ce2984253b207038ae0df9e9a2023-11-23T23:46:14ZengMDPI AGSensors1424-82202022-02-01225178910.3390/s22051789Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning ApproachChiara Filippini0Adolfo Di Crosta1Rocco Palumbo2David Perpetuini3Daniela Cardone4Irene Ceccato5Alberto Di Domenico6Arcangelo Merla7Department of Neurosciences, Imaging and Clinical Sciences, University G. D’Annunzio of Chieti-Pescara, 9, 66100 Chieti, ItalyDepartment of Psychological, Health and Territorial Sciences, University G. D’Annunzio of Chieti-Pescara, 9, 66100 Chieti, ItalyDepartment of Psychological, Health and Territorial Sciences, University G. D’Annunzio of Chieti-Pescara, 9, 66100 Chieti, ItalyDepartment of Neurosciences, Imaging and Clinical Sciences, University G. D’Annunzio of Chieti-Pescara, 9, 66100 Chieti, ItalyDepartment of Neurosciences, Imaging and Clinical Sciences, University G. D’Annunzio of Chieti-Pescara, 9, 66100 Chieti, ItalyDepartment of Neurosciences, Imaging and Clinical Sciences, University G. D’Annunzio of Chieti-Pescara, 9, 66100 Chieti, ItalyDepartment of Psychological, Health and Territorial Sciences, University G. D’Annunzio of Chieti-Pescara, 9, 66100 Chieti, ItalyDepartment of Neurosciences, Imaging and Clinical Sciences, University G. D’Annunzio of Chieti-Pescara, 9, 66100 Chieti, ItalyExtensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while extremely desirable, an accurate and automated emotion classification approach remains a challenging issue. To this end, this study presents an automated emotion recognition model based on easily accessible physiological signals and deep learning (DL) approaches. As a DL algorithm, a Feedforward Neural Network was employed in this study. The network outcome was further compared with canonical machine learning algorithms such as random forest (RF). The developed DL model relied on the combined use of wearables and contactless technologies, such as thermal infrared imaging. Such a model is able to classify the emotional state into four classes, derived from the linear combination of valence and arousal (referring to the circumplex model of affect’s four-quadrant structure) with an overall accuracy of 70% outperforming the 66% accuracy reached by the RF model. Considering the ecological and agile nature of the technique used the proposed model could lead to innovative applications in the affective computing field.https://www.mdpi.com/1424-8220/22/5/1789affective computingemotion recognitioninfrared imagingthermal imaging |
| spellingShingle | Chiara Filippini Adolfo Di Crosta Rocco Palumbo David Perpetuini Daniela Cardone Irene Ceccato Alberto Di Domenico Arcangelo Merla Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach Sensors affective computing emotion recognition infrared imaging thermal imaging |
| title | Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach |
| title_full | Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach |
| title_fullStr | Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach |
| title_full_unstemmed | Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach |
| title_short | Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach |
| title_sort | automated affective computing based on bio signals analysis and deep learning approach |
| topic | affective computing emotion recognition infrared imaging thermal imaging |
| url | https://www.mdpi.com/1424-8220/22/5/1789 |
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