Multiclassifier System with Fuzzy Inference Method Applied to the Recognition of Biosignals in the Control of Bioprosthetic Hand

  • Marek KurzynskiEmail author
  • Andrzej Wolczowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8866)


The paper presents an original method of recognition of patient’s intention to move of hand prosthesis during the grasping and manipulation of objects. The proposed method is based on a 2-level multiclassier system (MCS) with base classifiers dedicated to EMG and MMG signals, and with combining mechanism using a dynamic ensemble selection (DES) scheme and competence function. Competence function of base classifier is determined using validation set in the two step procedure. The first step consists in creating competence set using the methods based on relating the response of the classifier with the response obtained by a random guessing. In the second step, the competence set is generalized to the whole feature space using the learning procedure based on the Mamdani-type fuzzy inference system. The performance of MCS with proposed competence measure was experimentally compared against four benchmark classification methods using real data concerning the recognition of six types of grasping movements. The system developed achieved the highest classification accuracies demonstrating the potential of MC system for the control of bioprosthetic hand.


Multiple classifier system Fuzzy inference method Biosignals Prosthetic hand 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alpaydin, E.: Combined 5x2cv F test for comparing supervised classification learning algorithms. Neural Computation 11, 1885–1992 (1999)CrossRefGoogle Scholar
  2. 2.
    Dubois, D., Prade, H.: Fuzzy sets and systems. Academic Press, New York (1988)Google Scholar
  3. 3.
    Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley-Interscience (2001)Google Scholar
  4. 4.
    Krysmann, M., Kurzynski, M.: Methods of learning classifier competence applied to the dynamic ensemble selection. Advances in Intelligent Systems and Computing 226, 151–160 (2013)CrossRefGoogle Scholar
  5. 5.
    Kuncheva, I.: Combining pattern classifiers: Methods and Algorithms. Wiley-Interscience (2004)Google Scholar
  6. 6.
    Kurzynski, M., Wolczowski, A.: Classification of EMG Signals in a System for Training of Bioprosthetic Hand Control in One Side Handless Human. In: Proc. International Conference on Electrical Engineering and Computer Science, EECS 2012, Szanghai, pp. 566–576 (2012)Google Scholar
  7. 7.
    Kurzynski, M., Krysmann, M.: Fuzzy Inference Methods Applied to the Learning Competence Measure in Dynamic Classifier Selection. In: Proc. XXVII Conf. on Graphics, Patterns and Images. IEEE Comp. Society Press (in press, 2014)Google Scholar
  8. 8.
    Kurzynski, M., Wolczowski, A.: Multiple Classifier System Applied to the Control of Bioprosthetic Hand Based on Recognition of Multimodal Biosignals. In: Goh, J. (ed.) The 15th International Conference on Biomedical Engineering. IFMBE Proceedings, vol. 43, pp. 577–580. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Kurzynski, M., Wolczowski, A.: Hetero- and Homogeneous Multiclassifier Systems Based on Competence Measure Applied to the Recognition of Hand Grasping Movements. In: Piętka, E., Kawa, J., Wieclawek, W. (eds.) Information Technologies in Biomedicine. AISC, vol. 284, pp. 163–174. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  10. 10.
    Orizio, C.: Muscle sound: basis for the introduction of a mechanomyographic signal in muscle studies. Critical Reviews in Biomedical Engineering 21, 201–243 (1993)Google Scholar
  11. 11.
    Smits, P.: Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection. IEEE Trans. on Geoscience and Remote Sensing 40, 717–725 (2002)Google Scholar
  12. 12.
    Wolczowski, A., Kurzynski, M.: Control of dexterous hand via recognition of EMG signals using combination of decision-tree and sequential classifier. Advances in Soft Computing 45, 687–694 (2007)CrossRefGoogle Scholar
  13. 13.
    Wolczowski, A., Kurzynski, M.: Human-machine interface in bio-prosthesis control using EMG signal classification. Expert Systems 27, 53–70 (2010)CrossRefGoogle Scholar
  14. 14.
    Woloszynski, T., Kurzynski, M.: On a new measure of classifier competence applied to the design of multiclassifier systems. In: Foggia, P., Sansone, C., Vento, M. (eds.) ICIAP 2009. LNCS, vol. 5716, pp. 995–1004. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Woloszynski, T., Kurzynski, M.: A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recognition 44, 2656–2668 (2011)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

Personalised recommendations