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Conference Paper
A Study on Autonomic Nervous System Responses and Feauture Selection for Emotion Recognition - Emotion Recognition using Machine Learning Algorithms
- Authors
- Byoung-Jun Park, Eun-Hye Jang, Sang-Hyeob Kim, Myung-Ae Chung, Jin-Hun Sohn
- Issue Date
- 2014-03
- Citation
- International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS) 2014, pp.116-121
- Language
- English
- Type
- Conference Paper
- Abstract
- This study is related with emotion recognition based on autonomic nervous system responses. Three different emotional states, fear, surprise and stress, are evoked by stimuli and the autonomic nervous system responses for the induced emotions are measured as physiological signals such as skin temperature, electrodermal activity, electrocardiogram, and photoplethysmography. Twenty-eight features are analysed and extracted from these signals. The results of one-way ANOVA toward each parameter, there are significant differences among three emotions in some features. Therefore we select eight features from 28 features for emotion recognition. The comparative results of emotion recognition are discussed in view point of feature space with the selected features. For emotion recognition, we use four machine learning algorithms, namely, linear discriminant analysis, classification and regression tree, self-organizing map and na챦ve bayes, and those are evaluated by only training, 10-fold cross-validation and repeated random subsampling validation. This can be helpful to provide the basis for the emotion recognition technique in human computer interaction as well as contribute to the standardization in emotion-specific ANS responses. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.
- KSP Keywords
- Autonomic nervous system(ANS), Classification and regression tree(CART), Cross validation(CV), Electrodermal Activity, Emotion Recognition, Emotional states, Feature space, Human computer interaction, Machine Learning Algorithms, One-way ANOVA, Physiological signals
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC ND)
(CC BY NC ND)
