Intelligent systems and robots

Motion classification by artificial neural network for bionic hand control

V. F. Bez'yazichny^a, A. V. Yudin^a, M. V. Pankratov^a, E. A. Eliseichev^ab, P. S. Vorobyev^ab, I. S. Blinov<sup>ab

a</sup> Rybinsk State Aviation Technical University
^b "BioTech" LLC, Rybinsk, Russia

Abstract: The results of training and testing of an artificial neural network for recognizing human finger movements based on signals from electromyographic sensors are presented. Special attention is paid to the issues of preliminary processing of initial signals, including digital filtering, setting the optimal level corresponding to the resting state of the muscle, and calculation of signal attributes. In the paper, an envelope of the electromyographic signal was built on the basis of the “average energy” attribute, and the definition of muscle activity areas was carried out using two thresholds: adaptive in level and fixed in time. Three attributes are used directly for training the artificial neural network, which are specified depending on the requirements to the quality of training, either by indicator of distinguishability or by a complete enumeration of combinations of attributes. Optimization of the set of attributes for training the artificial neural network allowed achieving the level of correct answers more than 97%.

Keywords: artificial neural network, signal preprocessing, feature set, optimization, learning quality, electromyography, bionic hand.

DOI: 10.14357/20718594250103

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