Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/27589
Title: An information-theoretical method for emotion classification
Author: Brás, Susana
Carvalho, João M.
Barros, Filipa
Figueiredo, Cláudia
Soares, Sandra C.
Pinho, Armando J.
Keywords: Emotion
Affective computing
Classification
Data compression
Kolmogorov complexity
Issue Date: 25-Sep-2019
Publisher: Springer
Abstract: Identifying the emotion that someone is feeling will allow to improve the experience of the person interaction with environments, devices, and contents. Our body responds to events around us, by emotional responses, reflected in cognitive, behavioral and physiological dimensions. In the present work, we target the electrocardiogram (ECG) response as a mean to express emotions. Its processing is performed using information-theoretical measures, allowing true exploratory data mining. Participants recruited for the experiment watched three video sets in three different days, with a different emotion being induced in each day: fear, happiness, and neutral condition. The method is divided in: (1) conversion of the real-valued ECG record into a symbolic time-series; (2) relative compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as a reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. An accuracy of 90% was obtained. A posteriori analysis of the false negative results indicated that there was a relation between the relative dissimilarity measure and the self-reported emotions.
Peer review: yes
URI: http://hdl.handle.net/10773/27589
DOI: 10.1007/978-3-030-31635-8_30
ISBN: 978-3-030-31634-1
Appears in Collections:IEETA - Capítulo de livro
GOVCOPP - Capitulo de livro
CINTESIS - Capítulo de livro
WJCR - Capítulo de livro

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