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Emotion recognition system using short-term monitoring of physiological signals
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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“The patch categorizes the 4 kinds of human emotions (surprise, anger, stress, and sadness) based on singular vector machine (SVM) algorithm17.”
From A Flexible and Wearable Human Stress Monitoring Patch (2016) · cites this paper as [17]
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Metadata
- 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
- Published
- 2004-05
- Volume
- 42
- Issue
- 3
- Pages
- 419-427
- ISSN
- 0140-0118 (ISSN-L)
- 1741-0444
- 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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