Dimensional Affect Recognition from HRV: an Approach Based on Supervised SOM and ELM

• Autores: Bugnon, Leandro Ariel; Calvo, Rafael; Milone, Diego Humberto
• Año de publicación: 2017
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Physiological Measures; Affect Sensing And Analysis; Supervised Self-Organization; Extream Learning Machines; Dimensional Affect Estimation
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

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.
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
Fil: Calvo, Rafael. University of Sydney; Australia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
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