標題: STATISTICAL PREDICTION OF EMOTIONAL STATES BY PHYSIOLOGICAL SIGNALS WITH MANOVA AND MACHINE LEARNING
作者: Chueh, Tung-Hung
Chen, Tai-Been
Lu, Henry Horng-Shing
Ju, Shan-Shan
Tao, Teh-Ho
Shaw, Jiunn-Haur
統計學研究所
Institute of Statistics
關鍵字: Emotion recognition;physiological signals;machine learning;daily effect;MANOVA
公開日期: 1-六月-2012
摘要: For the importance of communication between human and machine interface, it would be valuable to develop an implement which has the ability to recognize emotional states. In this paper, we proposed an approach which can deal with the daily dependence and personal dependence in the data of multiple subjects and samples. 30 features were extracted from the physiological signals of subject for three states of emotion. The physiological signals measured were: electrocardiogram (ECG), skin temperature (SKT) and galvanic skin response (GSR). After removing the daily dependence and personal dependence by the statistical technique of MANOVA, six machine learning methods including Bayesian network learning, naive Bayesian classification, SVM, decision tree of C4.5, Logistic model and K-nearest-neighbor (KNN) were implemented to differentiate the emotional states. The results showed that Logistic model gives the best classification accuracy and the statistical technique of MANOVA can significantly improve the performance of all six machine learning methods in emotion recognition system.
URI: http://dx.doi.org/10.1142/S0218001412500085
http://hdl.handle.net/11536/20519
ISSN: 0218-0014
DOI: 10.1142/S0218001412500085
期刊: INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Volume: 26
Issue: 4
結束頁: 
顯示於類別:期刊論文


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