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Emotion Recognition of EMG Based on Improved L-M BP Neural Network and SVM | Yang | Journal of Software
Journal of Software, Vol 6, No 8 (2011), 1529-1536, Aug 2011
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

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.


Keywords


surface electromyography (emg) signal; emotional pattern recognition; support vector machine (svm); wavelet transform; l-m algorithm

References


[1] FrigoC, FerrarinM, FrassonW, et al. EMG signals detection and processing for on-line control of functional electrical stimulation [J]. Electromyogram & Kinesiology, 2000, 10 (5): 351–360.
http://dx.doi.org/10.1016/S1050-6411(00)00026-2

[2] J. A. Healey: Wearable and Automotive Systems for Affect Recognition from Physiology, PhD thesis, MIT, Cambridge, MA, May 2000

[3] R. W. Picard, E. Vyzas, and J. Healey: Toward Machine Emotional Intelligence: Analysis of Affective Physiological State, IEEE Transactions Pattern Analysis and Machine Intelligence, Vol.23, No.10, pp.1175-1191, Oct. 2001
http://dx.doi.org/10.1109/34.954607

[4] A. Haag, S. Goronzy, P. Schaich, J. Williams: Emotion Recognition Using Bio-Sensors: First Step Towards an Automatic System, Affective Dialogue Systems, Tutorial and ResearchWorkshop, Kloster Irsee, Germany, June 14-16, 2004

[5] F. Nasoz, K. Alvarez, C. L. Lisetti, N. Finkelstein: Emotion Recognition from Physiological Signals for Presence Technologies, International Journal of Cognition, Technology and Work, Special Issue on Presence, Vol 6(1), 2003

[6] Xinliang Zhang, Yonghong Tan. The adaptive control using BP neural networks for a nonlinear servo-motor [J]. Journal of Control Theory and Applications, 2008, 6(3): 273-276.
http://dx.doi.org/10.1007/s11768-008-6069-3

[7] WU Fang-liang, SHI Zhong-kun, YANG Xiang-hui, et al. Submarine Sonar Self-Noise Forecast Based on BP Neural Network and Levenberg-Marquart Algorithm [J].Shipbuilding of China, 2006, 47(3): 45- 50.

[8] ZHANG Kun WANG Zhi-zhong. The Application of BP Neural Network Improved with LM Algorithm in Surface EMG Signal Classification[J]. Chinese Journal of Medical Instrumentation, 2005, 29(6): 399-401.
PMid:16494048

[9] K. H. Kim, S. W. Bang, S. R. Kim. Emotion recognition system using short-term monitoring of physiological signals [J]. Med. Biol. Eng. Comput., 2004, 42, 419–427
http://dx.doi.org/10.1007/BF02344719

[10] YANG RuiQing, LIU Guang Yuan. Emotion Recognition Using Four Physiological Signals Based on BPSO[J]. Computer Science,, 2008, (03), 137-138.

[11] Pao, Y. H., Adaptive Pattern Recognition and Neural Networks, Addison-Wesley, Reading, MA, 1989.

[12] Wagner J, Kim J, André E, et al. From physiological signals to emotions: implementing and comparing selected methods for feature extraction and classification [C], IEEE International Conference on Multimedia & Expo, New York, 2005, 940-943.
http://dx.doi.org/10.1109/ICME.2005.1521579

[13] Federica Cavicchio, Massimo Poesio. Annotation of Emotion in Dialogue: The Emotion in Cooperation Project [J]. Lecture Notes in Computer Science, 2008, 5078: 233-239.
http://dx.doi.org/10.1007/978-3-540-69369-7_26

[14] Joyce H.D.M. Westerink, Egon L. van den Broek, Marleen H. Schut, et al. COMPUTING EMOTION AWARENESS THROUGH GALVANIC SKIN RESPONSE AND FACIAL ELECTROMYOGRAPHY[J]. Philips Research, 2008, 8(2): 149-162.
http://dx.doi.org/10.1007/978-1-4020-6593-4_14


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