Abstract
Pattern recognition of hand gesture is currently research hot spot. It is important for rehabilitation training, human-computer interaction, prosthetic control and sports science research etc. The brachioradialis, extensor digitorum communis, flexor carpi ulnaris muscle and flexor carpi radialis muscle as signal acquisition points; this paper captures four channel sEMG signals. Aiming at the sEMG signals of hand gesture, this paper uses the eigenvalue processed by RMS and MOV as training data samples, which is regarded as the input of LVQ neural network. Through training and learning samples, the better training result is got. The results of the study indicate that the LVQ neural network can effectively identify three action modes, all fingers, relax and middle, by adopting the four channel sEMG signals. The simple algorithm, small calculation and more than 89 percent recognition rate shows that it is a very good method of pattern recognition.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Vigouroux, L., Quaine, F., Labarre-Vila, A., et al.: Using EMG Data to Constrain Optimization Procedure Improves Finger Tendon Tension Estimations During Static Fingertip Force Production. Journal of Biomechanics 40(13), 2846–2856 (2007)
Murakami, K., Ta Guchi, H.: Gesture recognition using recurrent neural network. In: Proc. of the CHI 1991, pp. 237–241. ACM Press, New York (1991)
Chen, Y., Wang, H., Xue, A.: Passivity analysis of stochastic time-delay neural networks. Nonlinear Dyn. 61, 71–82 (2010)
Yu, W., Li, X.: Passivity Analysis of Dynamic Neural Networks. Neural Processing Letters 25, 143–155 (2007)
Li, Y., Chen, X., Zhang, X., Yang, J.: Hand Gesture SEMG Signal Recognition Based on ART2 Neutral Network. Journal of University Of Science And Technology of China 40(8), 829–834 (2010)
Yang, A., Chang, D.: Hand Gesture Recognition System Based on Neural Network and Hidden Markov Model. Electronic Measurement Technology 33(4), 60–64 (2010)
Luo, Z.-Z., Xiong, J., Liu, Z.-H.: Pattern Recognition of Hand Motions Based on WPT and LVQ. Pattern Recognition and Artificial Intelligence 23(5), 695–700 (2010)
Hou, W.-S., Ma, L., Wu, X.-Y., Zheng, X.-L., Peng, C.-L.: Motion Identification of Finger Based on Auto – regressive Model and Learning - vector - quantization Neural Network. Chinese Journal of Ergonomics 15(3), 18–21 (2009)
Fecit technology product research and development center: Neural Network and MatLab7 Application. Publishing House of Electronics Industry, Beijing (2005)
Wu, D., Sun, X., Zhang, Z.-C., Du, Z.-J.: Feature Collection and Analysis of Surface Electromyography Signals. Journal of Clinical Rehabilitative Tissue Engineering Research 14(43), 8073–8076 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zheng, X., Guo, Y., Wang, H. (2011). Pattern Recognition of Hand Gesture Based on LVQ Neutral Network. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_71
Download citation
DOI: https://doi.org/10.1007/978-3-642-23896-3_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23895-6
Online ISBN: 978-3-642-23896-3
eBook Packages: Computer ScienceComputer Science (R0)