Analysis of physiological signals for recognition of boredom, pain, and surprise emotions
Open accessAbstract
BACKGROUND: The aim of the study was to examine the differences of boredom, pain, and surprise. In addition to that, it was conducted to propose approaches for emotion recognition based on physiological signals. METHODS: Three emotions, boredom, pain, and surprise, are induced through the presentation of emotional stimuli and electrocardiography (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmography (PPG) as physiological signals are measured to collect a dataset from 217 participants when experiencing the emotions. Twenty-seven physiological features are extracted from the signals to classify the three emotions. The discriminant function analysis (DFA) as a statistical method, and five machine learning algorithms (linear discriminant analysis (LDA), classification and regression trees (CART), self-organizing map (SOM), Naïve Bayes algorithm, and support vector machine (SVM)) are used for classifying the emotions. RESULTS: The result shows that the difference of physiological responses among emotions is significant in heart rate (HR), skin conductance level (SCL), skin conductance response (SCR), mean skin temperature (meanSKT), blood volume pulse (BVP), and pulse transit time (PTT), and the highest recognition accuracy of 84.7% is obtained by using DFA. CONCLUSIONS: This study demonstrates the differences of boredom, pain, and surprise and the best emotion recognizer for the classification of the three emotions by using physiological signals.
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Jang et al. observed high recognition accuracy of 84.7%.
“High-observed recognition accuracy was also in research by Jang et al.: 84.7% [58].”
From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [58]
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References 53 · Cited by 159
Research works printed but not linked: 3
- [2]Ekman E Davidson RJ The nature of emotions, fundamental questions 1994 Oxford Oxford University Press
- [4]Frijda N The emotions 1986 Cambridge Cambridge University Press
- [30]Takahashi K. Remarks on emotion recognition from bio-potential signals. 2nd International Conference on Autonomous Robots and Agents, 2004 December, Palmerston North, New Zealand.
- Authors
- Jang, E.-H., Park, B.-J., Park, M.-S., Kim, S.-H., & Sohn, J.-H.
- Affiliations
- Electronics and Telecommunications Research Institute (KR)
- Chungnam National University (KR)
- Corresponding author
- Sohn, Jin-Hun
- Venue
- Journal of Physiological Anthropology
- Published
- 2015-06-18
- Volume
- 34
- Issue
- 1
- Pages
- 25-25
- ISSN
- 1880-6791 (ISSN-L)
- 1880-6805
- PMC
- PMC4490654
- OpenAlex
- W1842328245
- Cited by
- 159
- References
- 53
- Citations by year
- 2026: 3 · 2025: 15 · 2024: 18 · 2023: 15 · 2022: 19 · 2021: 18 · 2020: 17 · 2019: 17 · 2018: 15 · 2017: 12 · 2016: 10
- FWCI
- 18.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of PHYSIOLOGICAL ANTHROPOLOGY (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Research Foundation
- Ministry of Science, ICT and Future Planning (B0132-15-1003)
- Institute for Information and Communications Technology Promotion (B0132-15-1003)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- emotion recognition, boredom, pain, surprise, physiological signals, electrocardiogram, electrodermal activity, skin temperature, photoplethysmography, heart rate, skin conductance, blood volume pulse, pulse transit time, discriminant function analysis, linear discriminant analysis, support vector machine, self-organizing maps, naive Bayes, classification and regression trees
- MeSH terms
- Adult, Boredom, Electrocardiography, Emotions, Female, Galvanic Skin Response, Humans, Male, Pain, Signal Processing, Computer-Assisted, Skin Temperature, Photoplethysmography, Young Adult, Support Vector Machine
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