Abstract
In this chapter we present an advanced method of recognition of patient’s intention to move the multi-articulated prosthetic hand during grasping and manipulation of objects in a skillful manner. The proposed method is based on a 2-level multiclassier system (MCS) with base classifiers dedicated to particular types of biosignals: electromyography (EMG) and mechanomyography (MMG) signals, and with combining mechanism using a dynamic ensemble selection scheme and probabilistic competence function. To improve the precision and reliability of prosthesis control, the feedback signal derived from the prosthesis sensors is used. We present two original concepts of using such a signal. In the 1st method, the feedback signal is treated as a source of information about a correct class of hand movement and competence functions of base classifiers are dynamically tuned according to this information. In the 2nd one, classification procedure is organized into multistage process based on a decision tree scheme and consequently, feedback signal indicating an interior node of a tree allows us to narrow down the set of classes. The performance of MCS with feedback signal were experimentally tested on real datasets. The obtained results show that developed methodology can be practically applied to design a control system for dexterous bioprosthetic hand.
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This work was financed from the National Science Center resources in 2012–2014 years as a research project No ST6/06168, and from Wroclaw University of Technology as a statutory project.
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Wolczowski, A., Kurzynski, M. (2015). Control of Hand Prosthesis Using Fusion of Biosignals and Information from Prosthesis Sensors. In: Borowik, G., Chaczko, Z., Jacak, W., Łuba, T. (eds) Computational Intelligence and Efficiency in Engineering Systems. Studies in Computational Intelligence, vol 595. Springer, Cham. https://doi.org/10.1007/978-3-319-15720-7_19
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DOI: https://doi.org/10.1007/978-3-319-15720-7_19
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