Cited by 944Emotion and Mood RecognitionHeart Rate Variability and Autonomic ControlFace and Expression Recognition

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Emotion recognition system using short-term monitoring of physiological signals

Kim, K. H., Bang, S. W., Kim, S. R.·2004·Medical & Biological Engineering & Computing

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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.

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Authors: Kim, K. H., Bang, S. W., & Kim, S. R.
Affiliations: Yonsei University (KR)
Samsung (South Korea) (KR)
Corresponding author: Kim, K. H.
ORCID: Kim, K. H.
Venue: Medical & Biological Engineering & Computing
Publisher: Springer Science and Business Media LLC
Published: 2004-05
Volume: 42
Issue: 3
Pages: 419-427
ISSN: 0140-0118 (ISSN-L)
1741-0444
DOI: 10.1007/bf02344719
OpenAlex: W2078671978
Cited by: 944
References: 41
Citations by year: 2026: 17 · 2025: 26 · 2024: 38 · 2023: 43 · 2022: 65 · 2021: 54 · 2020: 69 · 2019: 67 · 2018: 61 · 2017: 68 · 2016: 38 · 2015: 58 · 2014: 56 · 2013: 47 · 2012: 42
FWCI: 18.09
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Medical & Biological Engineering & Computing (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
Keywords: emotion recognition, physiological signals, electrocardiogram, skin temperature, electrodermal activity, feature extraction, support vector machine, autonomic nervous system
MeSH terms: Autonomic Nervous System, Child, Child, Preschool, Emotions, Humans, Monitoring, Physiologic, Signal Processing, Computer-Assisted, User-Computer Interface, Recognition, Psychology

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Emotion recognition system using short-term monitoring of physiological signals

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