SEMG Multi-Class Classification Based on S4VM Algorithm

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 219)


A method using small amount of labeled instants and large unlabeled ones simultaneously involved in the training during the sEMG classification obtained a better effect is strongly needed. This paper introduces the S4VM proposed by Li et al. into surface EMG pattern recognition with small labeled instants and extends to multi-class classification problems, which will represent the autoregressive model characteristic value of the human hand movements of the seven types of EMG signal as the object of classification. The experimental results show that the safety semi-supervised support vector machine is suitable for the multi-pattern classification of surface EMG signal with high accuracy and good robustness.


S4VM EMG Multi-class Classification 


  1. 1.
    Smith LH, Hargrove LJ, Lock BA, Kuiken TA (2011) Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification errorand controller delay. IEEE Trans Neural Syst Rehabil Eng 19(2):1323–1326Google Scholar
  2. 2.
    Lalitha A, Thakor NV (2012) Design of an accelerometer-controlled myoelectric human computer interface. Adv Mater Res 12(14):403–408Google Scholar
  3. 3.
    Alkan A (2012) Mucahid Gunay identification of EMG signals using discriminant analysis and SVM classifier. Expert Syst Appl 39(12):44–47CrossRefMathSciNetGoogle Scholar
  4. 4.
    Lucas MF, Gaufriau A, Pascual S, Doncarli C, Farina D (2008) Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization. Biomedical Signal Proc Control 3(21):169–174Google Scholar
  5. 5.
    Katsisa CD, Exarchos TP, Papaloukas C, Goletsis Y, Fotiadis DI, Sarmas I (2007) A two-stage method for MUAP classification based on EMG decomposition. Comp Biol Med 37(12):1232–1240Google Scholar
  6. 6.
    Feyereisl J (2012) Aickelin, Uwe, Privileged information for data clustering. Inf Sci 194(24):4–23CrossRefGoogle Scholar
  7. 7.
    Chapelle O, Scholkopf B, Zien A (2006) Semi-supervised learning, vol 12, issue no 7. MIT Press, Cambridge, pp 2345–2349Google Scholar
  8. 8.
    Joachims T (1999) Transductive inference for text classification using support vector machines. In: Proceedings of the sixteenth international conference on machine learning, vol 21, issue no 8, Morgan Kaufmann Publishers, San Francisco, pp 200–209Google Scholar
  9. 9.
    Wang L, Chan K, Zhang Z (2003) Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval. In CVPR 26(54):629–634Google Scholar
  10. 10.
    Chapelle O, Sindhwani V, Keerthi SS (2008) Optimization techniques for semi-supervised support vector machines. J Mach Learn Res 9(5):203–233MATHGoogle Scholar
  11. 11.
    Li Y-F, Zhou Z-H (2011) Towards making unlabeled data never hurt. Proceedings of the 28th international conference on machine learning. ICML 73:836–838Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.School of Communication Engineering, Jilin UniversityChangchunChina
  2. 2.Key Laboratory of Bionic EngineeringMinistry of Education Jilin UniversityJilinChina

Personalised recommendations