Detalhes bibliográficos
| Ano de defesa: |
2019 |
| Autor(a) principal: |
Souza Júnior, Milton Machado de
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| Orientador(a): |
Azevedo, Dario Francisco Guimarães de
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| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
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| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Pontifícia Universidade Católica do Rio Grande do Sul
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| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Engenharia Elétrica
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| Departamento: |
Escola Politécnica
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| País: |
Brasil
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| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://tede2.pucrs.br/tede2/handle/tede/8452
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Resumo: |
This work aims to improve the recognition of emotional responses to different stimuli that provoke alterations into humans. Said alterations can be measured through several tools such as Magnetic Resonance Imaging, Functional Near Infrared Spectroscopy (fNIRS), Electroencephalography (EEG) and Facial Recognition. In this work, we used a combination of the fNIRS system, which measures the variation of hemoglobin oxygenation, and facial recognition tools. The participants were stimulated with a sequence of images from the IAPS database, which are labeled with the weighted emotions they provoke. This allowed the training of a classifier that was capable of predicting the emotion that was experienced by the user during the activity performed. This article covers the recognition of positive, negative and neutral emotions using classifiers, created in Matlab, that are based on participant physiological responses. The combination of fNIRS, facial recognition and machine learning supported the creation of a predictor with 77.2% correct classification rate. |
