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ThinkMind // ACHI 2013, The Sixth International Conference on Advances in Computer-Human Interactions // View article achi_2013_15_40_20226


Emotion Recognition using Autonomic Nervous System Responses

Authors:
Byoung-Jun Park
Eun-Hye Jang
Sang-Hyeob Kim
Chul Huh
Myoung-Ae Chung
Jin-Hun Sohn

Keywords: emotion; recognition; stimuli; physiological signal; autonomic nervous system responses; neural networks

Abstract:
Recently in HCI research, emotion recognition is one of the core processes to implement emotional intelligence. There are many studies using physiological signals in order to recognize human emotions. The purpose of this study is to recognize emotions using autonomic nervous system responses induced by three different emotions (boredom, pain and surprise). Three different emotional states are evoked by emotional stimuli, physiological signals (EDA, ECG, PPG and SKT) for the induced emotions are measured as the reactions of stimuli, and 27 features are extracted from their physiological signals for emotion recognition. The stimuli are used to provoke emotions and tested their appropriateness and effectiveness. Audio-visual film clips used as stimuli are captured originally from movies, documentary, and TV shows with the appropriateness of 86%, 97.3% and 94.1% for boredom, pain and surprise, respectively, and the effectiveness of 5.23 for happiness, 4.96 for pain and 6.12 for surprise (7 point Likert scale). Also, for the three emotion recognition, we propose a Fuzzy c-means clustering based neural networks using the physiological signals. The proposed model consists of three layers, namely, input, hidden and output layers. Here, fuzzy c-means clustering method, two types of polynomial and linear combination function are used as a kernel function in the input layer, the hidden layer and the output layer of neural networks, respectively. To evaluate the performance of emotion recognition of the proposed model, we use the 10-fold cross validation and a comparative analysis shows that the proposed model exhibit higher accuracy when compared with some other models that exist in the literature.

Pages: 389 to 394

Copyright: Copyright (c) IARIA, 2013

Publication date: February 24, 2013

Published in: conference

ISSN: 2308-4138

ISBN: 978-1-61208-250-9

Location: Nice, France

Dates: from February 24, 2013 to March 1, 2013

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