Emotion Recognition Using Physiological Signals

  • Lan Li
  • Ji-hua Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4282)


The ability to recognize emotion is one of the hallmarks of emotional intelligence. This paper proposed to recognize emotion using physiological signals obtained from multiple subjects without much discomfort from the body surface. Film clips were used to elicit target emotions and an emotion elicitation protocol, verified to be effective in the preliminary study, was provided. Four physiological signals, electrocardiogram (ECG), skin temperature (SKT), skin conductance (SC) and respiration were selected to extract 22 features for recognition. We collected a set of data from 60 female undergraduates when experiencing the target emotion. Canonical correlation analysis was adopted as a pattern classifier, and correct-classification ratio is 85.3%. The research indicated the feasibility of user-independent emotion recognition using physiological signals. But before emotion interpretation can occur at the level of human abilities, there still remains much work to be done.


Support Vector Machine Emotion Recognition Physiological Signal Emotional Intelligence Canonical Correlation Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lan Li
    • 1
  • Ji-hua Chen
    • 2
  1. 1.School of Electrical and Information EngineeringJiangsu UniversityZhenjiangP.R. China
  2. 2.Institute of Biomedical EngineeringJiangSu UniversityZhenJiangP.R. China

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