FORECESSION ELECTROMYOGRAPHY RECOGNITION AND GESTURES SELECTION FOR PROTESIS CONTROL

The relevance of this study is due to one of the main problems existing today in the field of building man-machine interfaces - is the creation of an effective management system that interacts directly with the user and external devices replacing functions (prostheses, wheelchairs, etc.). In this regard, this work is devoted to the study of the possibility of using physiological gestures from the daily life of a person to control the prosthesis with the safety of the forearm for at least one third. The leading approach to the study of this problem is the use of methods of statistical processing of experimental data, digital signal processing, machine learning algorithms and pattern recognition. This approach allows a comprehensive study of the electromyogram (EMG) of the forearm when making voluntary movements at different levels of the implementation of the myo-control system. The article presents the results of the EMG study recorded for 11 arbitrary movements from a group of subjects, describes the procedure for pre-processing the EMG and identifying characteristic features for signal recognition, discloses a method for classifying movements using an artificial neural network based on radial basic functions (RBF). Eight of the most suitable for classification movements were identified and ranked according to the classification accuracy: relaxation (like zero movement), hand opening, fist, hand flexion, hand supination, hand extension, hand pronation, pinch. The materials of the article are of practical value for building systems based on the human-machine interface, as well as for classification tasks in electrophysiology applications.

1. Pylatiuk C., Mueller-Riederer M., Kargov A., S. Schulz, O. Schill, M. Reischl, G.Bretthauer, Comparison of surface EMG monitoring electrodes for long-termuse in rehabilitation device control, in: IEEE 11th International Conference onRehabilitation Robotics, vols. 1 and 2, ICORR, Kyoto, Japan, June 23–26, 2009,pp. 348–352, http://dx.doi.org/10.1109/ICORR.2009.5209576;

2. The method and system of management of intellectual bionic limb Sposob i sistema upravleniya intellektual'noj bionicheskoj konechnost'yu [Текст]: RU2635632C1; Ivanyuk N.M., Karimov V.R., Budko R.YU., Gronskij P.V., Klejman S.M.;

3. Oskoei M.A., Hu H., Myoelectric control systems – a survey, Biomed.Signal Process. Control (2007) 275–294, http://dx.doi.org/10.1016/j.bspc.2007.07.009;

4. Bol'shakov V. A., Burov G. N. To the question of the formation of a prosthesis control system for amputation defects within the forearm // Vestnik vserossijskoj gil'dii protezistov-ortopedov № 58, 2014;

5. Phinyomark A., Phukpattaranont P., Limsakul C., Investigating long-term effects of feature extraction methods for continuous EMG pat-tern classification, Fluct. Noise Lett. 11 (4.) (2012), http://dx.doi.org/10.1142/S0219477512500289;

6. Kim K.S., Kang J.H., Lee Y.H., Thedevelopment of muscle training system using the electromyogram andinteractive game for physical rehabilitation, in: 5th Kuala LumpurInternational Conference on Biomedical Engineering 2011, IFMBE Proceed-ings, vol. 35, Kuala Lumpur, Malaysia, June 20– 23, 2011, pp. 801–804, http://dx.doi.org/10.1007/978-3-642-21729-6 196;

7. Young, J. Aaron, L.J. Hargrove, T.A. Kuiken, Improving myoelectric patternrecognition robustness to electrode shift by changing interelectrode distanceand electrode configuration, IEEE Trans. Biomed. Eng. 59 (3) (2012) 645–652, http://dx.doi.org/10.1109/TBME.2011.2177662;

8. Van den Broek EL, Lis'y V, Janssen JH, Westerink JHDM, Schut MH, Tuinenbreijer K: Affective Man-machine Interface: Unveiling human emotions through biosignals. Biomedical Engineering Systems and Technologies: Communications in Computer and Information Science. Berlin, Germany: Springer Verlag; 2010;

9. Maria C. Selection of suitable hand gestures for reliable myoelectric human computer interface / Maria Castro, Sridhar P Arjunan, Dinesh K Kumar // BioMedical Engineering OnLine. - 2015. - 14:30 (9 April 2015) available at: http://www.biomedical-engineering-online.com/content/14/1/30;

10. Budko R.Yu., Starchenko I.B. Sozdanie klassifikatora mimicheskikh dvizhenii na osnove analiza elektromiogrammy [Creation of the facial gestures сlassifier based on the electromyogram analysis]. Trudy SPIIRAN – SPIIRAS Proceedings, 2016, iss. 46, pp. 76–89.;

11. Budko R., Starchenko I., Budko A. Preprocessing data for facial gestures classifier on the basis of the neural network analysis of biopotentials muscle signals. Lecture Notes in Computer Science. 2016. Т. 9812. Springer, С. 163-171.