Abstract
In this paper the problem of recognition of patient’s intent to move hand prosthesis is addressed. The proposed method is based on recognition of electromyographic (EMG) and mechanomyographic (MMG) biosignals using a multiclassifier (MC) system working with dynamic ensemble selection scheme and original concept of competence measure. The concept focuses on developing competence and interclass cross- competence measures which can be applied as a method for classifiers combination. The cross-competence measure allows an ensemble to harness information obtained from incompetent classifiers instead of removing them from the ensemble. The performance of MC system with proposed competence measure was experimentally compared against six state-of-the-art 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.
Similar content being viewed by others
References
Berger, J.: Statistical Decision Theory and Bayesian Analysis. Springer, New York (1985). doi:10.1007/978-1-4757-4286-2
Britto, A., Sabourin, R., Oliveira, R.: Dynamic selection of classifiers a comprehensive review. Pattern Recogn. 47, 3665–3680 (2014)
Carrozza, M., Cappiello, G., et al.: Design of a cybernetic hand for perception and action. Biol. Cybern. 95, 626–644 (2006)
De Luca, C.: Electromyography. In: Webster, J.G. (ed.) Encyclopedia of Medical Devices and Instrumentation, pp. 98–109. Wiley, Hoboken (2006)
Demsar, J.: Statistical comparison of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Devroye, L.: A Probabilistic Theory of Pattern Recognition. Springer, New York (1996). doi:10.1007/978-1-4612-0711-5
Dietterich, T.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10, 1895–1923 (1998)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley, New York (2000)
Englehart, K., Hudgins, B.: A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 50, 848–854 (2003)
Kakoty, M., Hazarika, S.: Towards electromyogram-based grasps classification. Int. J. Biomech. Biomed. Robot. 3(2), 63–73 (2014)
Khushaba, R.: Application of biosignal-driven intelligent systems for multifunction prosthesis control. Ph.D. thesis, Faculty of Engineering and Information Technology, University of Technology, Sydney (2010)
Ko, A., Sabourin, N., Britto, A.: From dynamic classifier selection to dynamic ensemble selection. Pattern Recogn. 41, 1718–1731 (2008)
Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, Hoboken (2004)
Kurzynski, M.: On a two-level multiclassifier system with error correction applied to the control of bioprosthetic hand. In: Proceedings of the 14th World Congress of Medical Informatics MEDINFO, p. 210 (2013)
Kurzynski, M., Wolczowski, A.: Multiclassifier system with fuzzy inference method applied to the recognition of biosignals in the control of bioprosthetic hand. In: Zeng, Z., Li, Y., King, I. (eds.) ISNN 2014. LNCS, vol. 8866, pp. 469–478. Springer, Cham (2014). doi:10.1007/978-3-319-12436-0_52
Kurzynski, M., Krysmann, M., et al.: Multiclassifier system with hybrid learning applied to the control of bioprosthetic hand. Comput. Biol. Med. 69, 286–297 (2016)
Mamoni, D.: On cardinality of fuzzy sets. Int. J. Intell. Syst. Appl. 5, 47–52 (2013)
Micera, C., Carpantero, J., Raspopovic, S.: Control of hand prostheses using peripheral information. IEEE Rev. Biomed. Eng. 3, 48–68 (2010)
Oskoei, M., Hu, H.: Support vector machine-based classification scheme for EMG control applied to upper limb. IEEE Trans. Biomed. Eng. 55, 1956–1965 (2008)
Peerdeman, B., Boere, D., et al.: Myoelectric forearm prostheses: state of the art from a user-centered perspective. J. Rehabil. Res. Dev. 48, 719–738 (2011)
Ravindra, K., Ildstad, S.: Immunosuppressive protocols and immunological challenges related to hand transplantation. Hand Clin. 27(4), 467–79 (2011)
Schloegl, A.: A comparison of multivariate autoregressive estimators. Sig. Process. 9, 2426–2429 (2006)
Trajdos, P., Kurzynski, M.: A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier. Int. J. Appl. Math. Comput. Sci. 26, 17–28 (2016)
Wolczowski, A., Kurzynski, M.: Human - machine interface in bio-prosthesis control using EMG signal classification. Expert Syst. 27, 53–70 (2010)
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). doi:10.1007/978-3-642-04146-4_106
Woloszynski, T., Kurzynski, M.: A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recogn. 44, 2656–2668 (2011)
Woloszynski, T., Kurzynski, M., et al.: A measure of competence based on random classification for dynamic ensemble selection. Inf. Fusion 13, 207–213 (2012)
Woloszynski, T.: Matlab Central File Enchange (2010). http://www.mathwork.com/matlabcentral/fileenchange/28391-classifier-competence-based-on-probabilistic-modeling
Wolpert, D.: Stacked generalization. Neural Netw. 5, 214–259 (1992)
Woods, K., Kegelmeyer, W., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 19, 405–410 (1997)
Acknowledgment
This work was supported by the statutory funds of the Dept. of Systems and Computer Networks, Wroclaw Univ. of Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Kurzynski, M., Trajdos, P., Wolczowski, A. (2017). Multiclassifier System Using Class and Interclass Competence of Base Classifiers Applied to the Recognition of Grasping Movements in the Control of Bioprosthetic Hand. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-65340-2_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65339-6
Online ISBN: 978-3-319-65340-2
eBook Packages: Computer ScienceComputer Science (R0)