Statistical prediction of emotional states by physiological signals with manova and machine learning

Tung Hung Chueh*, Tai Been Chen, Henry Horng Shing Lu, Shan Shan Ju, Teh Ho Tao, Jiunn Haur Shaw

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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.

Original languageEnglish
Article number12500085
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number4
StatePublished - 1 Jun 2012


  • daily effect
  • Emotion recognition
  • machine learning
  • physiological signals


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