Multiclassifier System Using Class and Interclass Competence of Base Classifiers Applied to the Recognition of Grasping Movements in the Control of Bioprosthetic Hand

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10423)


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.


Bioprosthesis EMG signal MMG signal Multiclassifier system Competence measure 



This work was supported by the statutory funds of the Dept. of Systems and Computer Networks, Wroclaw Univ. of Technology.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

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