Emotion recognition from physiological signals
Open accessAbstract
Emotion recognition is one of the great challenges in human-human and human-computer interaction. Accurate emotion recognition would allow computers to recognize human emotions and therefore react accordingly. In this paper, an approach for emotion recognition based on physiological signals is proposed. Six basic emotions: joy, sadness, fear, disgust, neutrality and amusement are analysed using physiological signals. These emotions are induced through the presentation of International Affecting Picture System (IAPS) pictures to the subjects. The physiological signals of interest in this analysis are: electromyogram signal (EMG), respiratory volume (RV), skin temperature (SKT), skin conductance (SKC), blood volume pulse (BVP) and heart rate (HR). These are selected to extract characteristic parameters, which will be used for classifying the emotions. The SVM (support vector machine) technique is used for classifying these parameters. The experimental results show that the proposed methodology provides in general a recognition rate of 85% for different emotional states.
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Previous studies found general recognition rates from 57.77% to 85.46% for different emotional states.
“Previous studies have provided evidence for general recognition rate from 57,77% to 85,46% for different emotional states”
From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [65]
A larger set of analyzed signals and more recognized emotions provides better results.
“that more higher set of analyzed signals and more recognized emotions provides better results [64][65][66].”
From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [65]
Cites
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382 · see citing works
Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System
Haag, A., Goronzy, S., Schaich, P., & Williams, J. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24842-2_4 · Cited by 451 · see citing works
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
Kuncheva, L. I., & Vetrov, D. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.226 · Cited by 327 · see citing works
Genetic-Based Type II Feature Extraction for Periocular Biometric Recognition: Less is More
Adams, J., Woodard, D. L., Dozier, G. V., et al. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.59 · Cited by 84 · see citing works
A User Independent, Biosignal Based, Emotion Recognition Method
Rigas, G., Katsis, C. D., Ganiatsas, G., & Fotiadis, D. I. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73078-1_36 · Cited by 75 · see citing works
Towards the smart sensors based human emotion recognition
Quazi, M. T., Mukhopadhyay, S. C., Suryadevara, N. K., & Huang, Y. M. (2012) · 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings · doi:10.1109/i2mtc.2012.6229646 · Cited by 49 · see citing works
Optimization and Improvement Based on K-Means Cluster Algorithm
Wu, J., & Yu, W. (2009) · International Symposium on Knowledge Acquisition and Modeling (KAM) · doi:10.1109/kam.2009.185 · Cited by 21 · see citing works
A comparative study of SVM kernel applied to emotion recognition from physiological signals
Maaoui, C., & Pruski, A. (2008) · International Multi-Conference on Systems, Signals and Devices · doi:10.1109/ssd.2008.4632891 · Cited by 15 · see citing works
Self-citationCultural Development and Information Science and Technology
Shi-jie, G. (2003) · Contemporary Chinese Thought · doi:10.2753/csp1097-146735028 · Cited by 1 · see citing works
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A machine learning model for emotion recognition from physiological signals
Domínguez-Jiménez, J. A., Campo-Landines, K. C., Martínez-Santos, J. C., Delahoz, E. J., & Contreras-Ortiz, S. H. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101646 · Cited by 232 · see citing works
Survey on AI-Based Multimodal Methods for Emotion Detection
Marechal, C., Mikołajewski, D., Tyburek, K., Prokopowicz, P., Bougueroua, L., Ancourt, C., & Węgrzyn-Wolska, K. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-16272-6_11 · Cited by 137 · see citing works
- Authors
- GOUIZI, K., BEREKSI REGUIG, F., & MAAOUI, C.
- Affiliations
- University of Abou Bekr Belkaïd (DZ)
- Université Paul Verlaine - Metz (FR)
- Corresponding author
- GOUIZI, K.
- ORCID
- MAAOUI, C.
- Venue
- Journal of Medical Engineering & Technology
- Publisher
- Informa UK Limited
- Published
- 2011-09-22
- Volume
- 35
- Issue
- 6-7
- Pages
- 300-307
- ISSN
- 0309-1902 (ISSN-L)
- 1464-522X
- OpenAlex
- W1989978397
- Cited by
- 92
- References
- 9
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 5 · 2023: 9 · 2022: 10 · 2021: 6 · 2020: 13 · 2019: 10 · 2018: 13 · 2017: 4 · 2016: 5 · 2014: 1 · 2013: 4 · 2012: 1
- FWCI
- 2.47
- Type
- journal article
- Language
- en
- Open access
- Locations
- Journal of Medical Engineering & Technology (publishedVersion)
- PubMed (publishedVersion)
- Dépôt Institutionnel de lUniversité de Tlemcen (submittedVersion)
- HAL (submittedVersion)
- 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 › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- emotion recognition, physiological signals, basic emotions, electromyography, respiratory volume, skin temperature, skin conductance, blood volume pulse, heart rate, support vector machine, emotion classification, human-computer interaction
- MeSH terms
- Adult, Electric Conductivity, Electromyography, Emotions, Female, Humans, Lung Volume Measurements, Male, Monitoring, Physiologic, Signal Processing, Computer-Assisted, Skin Temperature, Support Vector Machine
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