DocumentCode :
3516684
Title :
The Analysis of Emotion Recognition from GSR Based on PSO
Author :
Wu, Guanghua ; Liu, Guangyuan ; Hao, Min
Author_Institution :
Sch. of Electron. & Inf. Eng., Southwest Univ., Chongqing, China
fYear :
2010
fDate :
28-29 Oct. 2010
Firstpage :
360
Lastpage :
363
Abstract :
A method for recognizing the emotion states of subjects based on 30 features extracted from their Galvanic Skin Response (GSR) signals was proposed. GSR signals were acquired by means of experiments attended by those subjects. Next the data was normalized with the calm signal of the same subject after being de-noised. Then the normalized data were extracted features before the step of feature selection. Immune Hybrid Particle Swarm Optimization (IH-PSO) was proposed to select the feature subsets of different emotions. Classifier for feature selection was evaluated on the correct recognition as well as number of the selected features. At last, this paper verified the effectiveness of the feature subsets selected with another new data. All performed in this paper illustrate that IH-PSO can achieve much effective results, and further more, demonstrate that there is significant emotion information in GSR signal.
Keywords :
emotion recognition; feature extraction; medical signal processing; particle swarm optimisation; psychology; signal classification; GSR; PSO; emotion recognition; feature extraction; feature selection; galvanic skin response; immune hybrid particle swarm optimization; signal classification; Emotion recognition; Equations; Feature extraction; Optimization; Particle swarm optimization; Skin; Training; Emotion; GSR signal; IH-PSO;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligence Information Processing and Trusted Computing (IPTC), 2010 International Symposium on
Conference_Location :
Huanggang
Print_ISBN :
978-1-4244-8148-4
Electronic_ISBN :
978-0-7695-4196-9
Type :
conf
DOI :
10.1109/IPTC.2010.60
Filename :
5663259
Link To Document :
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