Mostrar el registro sencillo del ítem

dc.contributor.author
Bugnon, Leandro Ariel
dc.contributor.author
Calvo, Rafael
dc.contributor.author
Milone, Diego Humberto
dc.date.available
2018-06-06T19:56:23Z
dc.date.issued
2017-10
dc.identifier.citation
Bugnon, Leandro Ariel; Calvo, Rafael; Milone, Diego Humberto; Dimensional Affect Recognition from HRV: an Approach Based on Supervised SOM and ELM; Institute of Electrical and Electronics Engineers; IEEE Transactions on Affective Computing; 10-2017
dc.identifier.issn
1949-3045
dc.identifier.uri
http://hdl.handle.net/11336/47577
dc.description.abstract
Dimensional affect recognition is a challenging topic and current techniques do not yet provide the accuracy necessary for HCI applications. In this work we propose two new methods. The first is a novel self-organizing model that learns from similarity between features and affects. This method produces a graphical representation of the multidimensional data which may assist the expert analysis. The second method uses extreme learning machines, an emerging artificial neural network model. Aiming for minimum intrusiveness, we use only the heart rate variability, which can be recorded using a small set of sensors. The methods were validated with two datasets. The first is composed of 16 sessions with different participants and was used to evaluate the models in a classification task. The second one was the publicly available Remote Collaborative and Affective Interaction (RECOLA) dataset, which was used for dimensional affect estimation. The performance evaluation used the kappa score, unweighted average recall and the concordance correlation coefficient. The concordance coefficient on the RECOLA test partition was 0.421 in arousal and 0.321 in valence. Results shows that our models outperform state-of-the-art models on the same data and provides new ways to analyze affective states.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institute of Electrical and Electronics Engineers
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Physiological Measures
dc.subject
Affect Sensing And Analysis
dc.subject
Supervised Self-Organization
dc.subject
Extream Learning Machines
dc.subject
Dimensional Affect Estimation
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Dimensional Affect Recognition from HRV: an Approach Based on Supervised SOM and ELM
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-05-31T18:19:02Z
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Nueva York
dc.description.fil
Fil: Bugnon, Leandro Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.description.fil
Fil: Calvo, Rafael. University of Sydney; Australia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.journal.title
IEEE Transactions on Affective Computing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/8070380/
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/TAFFC.2017.2763943


Archivos asociados

Documento no disponible

Comunidades y colecciones

  • Articulos(SINC(I)) [43]
    Articulos de INST. DE INVESTIGACION EN SE?ALES, SISTEMAS E INTELIGENCIA COMPUTACIONAL

Mostrar el registro sencillo del ítem