Tipo
Artigos em Revista
Tipo de Documento
Artigo Completo
Título
metaFERA: a meta-framework for creating emotion recognition frameworks for physiological signals
Participantes na publicação
João Oliveira (Author)
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA
Soraia M. Alarcão (Author)
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA
Dep. Informática
Teresa Chambel (Author)
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA
Dep. Informática
LASIGE
Manuel J. Fonseca (Author)
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA
Dep. Informática
LASIGE
Resumo
Recognizing emotions from physiological signals has proven to be important in various scenarios. To assist in developing emotion recognizers, software frameworks and toolboxes have emerged, offering ready-to-use components. However,these have limitations regarding the type of physiological signals supported, the recognition steps covered, or the acquisition of multiple physiological signals. This paper presents metaFERA, an architectural meta-framework for creating software frameworks for end-to-end emotion recognition from physiological signals. The modularity and flexibility of the meta-framework and the resulting frameworks allow the fast prototyping of emotion recognition systems and experiments to test and validate new algorithms. To that end, metaFERA offers: (i) a set of pre-configured blocks to which we can add behavior to create framework components; (ii) an easy way to add behavior to the pre-configured blocks; (iii) a channel-based communication mechanism that transparently and efficiently supports the exchange of information between components; (iv) a simple and easy way to use and link components from a resulting framework to create applications. Additionally, we provide a set of Web services, already configured, to make the resulting recognition systems available as a service. To validate metaFERA, we created a framework for Electrodermal Activity, an emotion recognizer to identify high/low arousal using the aforementioned framework, and a layer to offer the recognizer as a service.
Data de Publicação
2023-06-26
Instituição
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA
Suporte
Multimedia Tools and Applications
Identificadores da Publicação
ISSN - 1380-7501
Editora
Springer Science and Business Media LLC
Identificadores do Documento
DOI -
https://doi.org/10.1007/s11042-023-15249-5
URL -
http://dx.doi.org/10.1007/s11042-023-15249-5
Identificadores de Qualidade
SCOPUS Q1 (2022) - 0.145 - Media Technology
SCIMAGO Q1 (2022) - 0.72 - Media Technology