AUTOMATIC EMOTION INDUCTION AND ASSESSMENT FRAMEWORK Enhancing User Interfaces ...

• Autor(es): Jorge Teixeira ; Vasco Vinhas ; Luís Paulo Reis ; Eugénio C. Oliveira
• Data: 2009
• Identificador Persistente: http://hdl.handle.net/10216/15142
• Origem: Repositório Aberto da Universidade do Porto
• Assunto(s): Ciências Físicas; Ciência de computadores; Informática
• Tipo de Documento: Documento de conferência
• Idioma: Português

Descrição

Emotion's definition, identification, systematic induction and efficient and reliable classification have been themes to which several complementary knowledge areas such as psychology, medicine and computer science have been dedicating serious investments. This project consists in developing an automatic tool for emotion assessment based on a dynamic biometric data acquisition set as galvanic skin response and electroencephalography arc practical examples. The output of standard emotional induction methods is the support for classification based on data analysis and processing. The conducted experimental sessions, alongside with the developed support tools, allowed the extraction on conclusions such as the capability of effectively performing automatic classification of the subject's predominant emotional state. Self assessment interviews validated the developed tool's success rate of approximately 75%. It was also experimentally strongly suggested that female subjects are emotionally more active and easily induced than mates. Emotion's definition, identification, systematic induction and efficient and reliable classification have been themes to which several complementary knowledge areas such as psychology, medicine and computer science have been dedicating serious investments. This project consists in developing an automatic tool for emotion assessment based on a dynamic biometric data acquisition set as galvanic skin response and electroencephalography arc practical examples. The output of standard emotional induction methods is the support for classification based on data analysis and processing. The conducted experimental sessions, alongside with the developed support tools, allowed the extraction on conclusions such as the capability of effectively performing automatic classification of the subject's predominant emotional state. Self assessment interviews validated the developed tool's success rate of approximately 75%. It was also experimentally strongly suggested that female subjects are emotionally more active and easily induced than mates.