Next Article in Journal
Cooperative Detection of Multiple Targets by the Group of Mobile Agents
Previous Article in Journal
Neural Computing Enhanced Parameter Estimation for Multi-Input and Multi-Output Total Non-Linear Dynamic Models
Previous Article in Special Issue
Particle Swarm Optimisation: A Historical Review Up to the Current Developments
Article

Emotional State Recognition from Peripheral Physiological Signals Using Fused Nonlinear Features and Team-Collaboration Identification Strategy

1
School of Mechanical Engineering, Changzhou University, Changzhou 213164, China
2
Remote Measurement and Control Key Lab of Jiangsu Province, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(5), 511; https://doi.org/10.3390/e22050511
Received: 25 March 2020 / Revised: 25 April 2020 / Accepted: 27 April 2020 / Published: 30 April 2020
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
Emotion recognition realizing human inner perception has a very important application prospect in human-computer interaction. In order to improve the accuracy of emotion recognition, a novel method combining fused nonlinear features and team-collaboration identification strategy was proposed for emotion recognition using physiological signals. Four nonlinear features, namely approximate entropy (ApEn), sample entropy (SaEn), fuzzy entropy (FuEn) and wavelet packet entropy (WpEn) are employed to reflect emotional states deeply with each type of physiological signal. Then the features of different physiological signals are fused to represent the emotional states from multiple perspectives. Each classifier has its own advantages and disadvantages. In order to make full use of the advantages of other classifiers and avoid the limitation of single classifier, the team-collaboration model is built and the team-collaboration decision-making mechanism is designed according to the proposed team-collaboration identification strategy which is based on the fusion of support vector machine (SVM), decision tree (DT) and extreme learning machine (ELM). Through analysis, SVM is selected as the main classifier with DT and ELM as auxiliary classifiers. According to the designed decision-making mechanism, the proposed team-collaboration identification strategy can effectively employ different classification methods to make decision based on the characteristics of the samples through SVM classification. For samples which are easy to be identified by SVM, SVM directly determines the identification results, whereas SVM-DT-ELM collaboratively determines the identification results, which can effectively utilize the characteristics of each classifier and improve the classification accuracy. The effectiveness and universality of the proposed method are verified by Augsburg database and database for emotion analysis using physiological (DEAP) signals. The experimental results uniformly indicated that the proposed method combining fused nonlinear features and team-collaboration identification strategy presents better performance than the existing methods. View Full-Text
Keywords: emotion recognition; physiological signals; nonlinear features; identification strategy emotion recognition; physiological signals; nonlinear features; identification strategy
Show Figures

Figure 1

MDPI and ACS Style

Pan, L.; Yin, Z.; She, S.; Song, A. Emotional State Recognition from Peripheral Physiological Signals Using Fused Nonlinear Features and Team-Collaboration Identification Strategy. Entropy 2020, 22, 511. https://doi.org/10.3390/e22050511

AMA Style

Pan L, Yin Z, She S, Song A. Emotional State Recognition from Peripheral Physiological Signals Using Fused Nonlinear Features and Team-Collaboration Identification Strategy. Entropy. 2020; 22(5):511. https://doi.org/10.3390/e22050511

Chicago/Turabian Style

Pan, Lizheng, Zeming Yin, Shigang She, and Aiguo Song. 2020. "Emotional State Recognition from Peripheral Physiological Signals Using Fused Nonlinear Features and Team-Collaboration Identification Strategy" Entropy 22, no. 5: 511. https://doi.org/10.3390/e22050511

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop