JSW 2011 Vol.6(8): 1529-1536 ISSN: 1796-217X
doi: 10.4304/jsw.6.8.1529-1536
doi: 10.4304/jsw.6.8.1529-1536
Emotion Recognition of EMG Based on Improved L-M BP Neural Network and SVM
Shanxiao Yang, Guangying Yang
Department of Electronics Engineering, Taizhou University, Taizhou, China
Abstract—This paper compares the emotional pattern recognition method between standard BP neural network classifier and BP neural network classifier improved by the L-M algorithm. Then we compare the method Support Vector Machine (SVM) to them. Experiment analyzes wavelet transform of surface Electromyography (EMG) to extract the maximum and minimum wavelet coefficients of multi-scale firstly. We then input the two kinds of classifier of the structural feature vector for emotion recognition. The experimental result shows that the standard BP neural network classifier, L-M improved BP neural network classifier and support vector machine’s overall pattern recognition rate is 62.5%, 83.33% and 91.67 respectively. Experimental result shows that feature vector extracted by the wavelet transform can characterize emotional patterns through the comparison with the BP neural network classifier and Support Vector Machine, indicating that the Support Vector Machine have a stronger emotional recognition effect.
Index Terms—Surface Electromyography (EMG) Signal; Emotional Pattern Recognition; Support Vector Machine (SVM); Wavelet Transform; L-M algorithm
Abstract—This paper compares the emotional pattern recognition method between standard BP neural network classifier and BP neural network classifier improved by the L-M algorithm. Then we compare the method Support Vector Machine (SVM) to them. Experiment analyzes wavelet transform of surface Electromyography (EMG) to extract the maximum and minimum wavelet coefficients of multi-scale firstly. We then input the two kinds of classifier of the structural feature vector for emotion recognition. The experimental result shows that the standard BP neural network classifier, L-M improved BP neural network classifier and support vector machine’s overall pattern recognition rate is 62.5%, 83.33% and 91.67 respectively. Experimental result shows that feature vector extracted by the wavelet transform can characterize emotional patterns through the comparison with the BP neural network classifier and Support Vector Machine, indicating that the Support Vector Machine have a stronger emotional recognition effect.
Index Terms—Surface Electromyography (EMG) Signal; Emotional Pattern Recognition; Support Vector Machine (SVM); Wavelet Transform; L-M algorithm
Cite: Shanxiao Yang, Guangying Yang, "Emotion Recognition of EMG Based on Improved L-M BP Neural Network and SVM," Journal of Software vol. 6, no. 8, pp. 1529-1536, 2011.
General Information
ISSN: 1796-217X (Online)
Frequency: Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKI, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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