Medical and Biological Engineering and Computing

, Volume 42, Issue 3, pp 419–427

Emotion recognition system using short-term monitoring of physiological signals

Authors

    • Department of Biomedical Engineering, College of Health ScienceYonsei University
  • S. W. Bang
    • Human-computer Interaction LaboratorySamsung Advanced Institute of Technology
  • S. R. Kim
    • Human-computer Interaction LaboratorySamsung Advanced Institute of Technology
Article

DOI: 10.1007/BF02344719

Cite this article as:
Kim, K.H., Bang, S.W. & Kim, S.R. Med. Biol. Eng. Comput. (2004) 42: 419. doi:10.1007/BF02344719

Abstract

A physiological signal-based emotion recognition system is reported. The system was developed to operate as a user-independent system, based on physiological signal databases obtained from multiple subjects. The input signals were electrocardiogram, skin temperature variation and electrodermal activity, all of which were acquired without much discomfort from the body surface, and can reflect the influence of emotion on the autonomic nervous system. The system consisted of preprocessing, feature extraction and pattern classification stages. Preprocessing and feature extraction methods were devised so that emotion-specific characteristics could be extracted from short-segment signals. Although the features were carefully extracted, their distribution formed a classification problem, with large overlap among clusters and large variance within clusters. A support vector machine was adopted as a pattern classifier to resolve this difficulty. Correct-classification ratios for 50 subjects were 78.4% and 61.8%, for the recognition of three and four categories, respectively.

Keywords

Emotion recognitionAutonomic nervous systemPhysiological signal processingSupport vector machine
Download to read the full article text

Copyright information

© IFMBE 2004