Title: Integration of affective computing techniques and soft computing for developing a human affective recognition system for U-learning systems

Authors: Chih-Hung Wu; Yi-Lin Tzeng; Bor-Chen Kuo; Gwo-Hshiung Tzeng

Addresses: Department of Digital Content and Technology, Graduate Institute of Educational Measurement and Statistics, National Taichung University of Education, Taichung City, Taiwan ' Department of Digital Content and Technology, Graduate Institute of Educational Measurement and Statistics, National Taichung University of Education, Taichung City, Taiwan ' Department of Digital Content and Technology, Graduate Institute of Educational Measurement and Statistics, National Taichung University of Education, Taichung City, Taiwan ' Graduate Institute of Urban Planning, College of Public Affairs, National Taipei University, New Taipei City, Taiwan

Abstract: In this study, a human affective norm (emotion and attention) recognition system for U-learning systems is developed. Fifth graders in an elementary school were recruited as participants firstly to see some emotional pictures from the International Affective Picture System (IAPS), and to do the attention test to obtain the affective information - electroencephalography (EEG) and electrocardiogram (ECG) for developing the affective norm recognition system of the study. These bio-physiology signals extract important features by using four types of linear Principal Component Analysis (PCA) to serve as the input variables for Support Vector Machine (SVM) model. The results of feature selection showed that factor analysis with covariance extraction method has higher accumulative variances than correlation extraction method. This study suggested that future researchers may try to adopt more non-linear feature selection methods in order to develop a high accuracy SVM-based emotion recognition system.

Keywords: affective computing; SVM; support vector machines; emotion recognition systems; EEG; electroencephalograms; ECG; electrocardiograms; ubiquitous learning; human affective recognition; u-learning; elementary schools; elementary education; feature extraction; bio-physiology signals; biosignals; principal component analysis; PCA; feature selection.

DOI: 10.1504/IJMLO.2014.059997

International Journal of Mobile Learning and Organisation, 2014 Vol.8 No.1, pp.50 - 66

Available online: 23 Mar 2014 *

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