Hybrid Method for Controlling Muscle Fatigue in the Robotic System-Reference-Cited by-同舟云学术

Hybrid Method for Controlling Muscle Fatigue in the Robotic System

Published:2024-01-27 Issue:3 Volume:13 Page:64-81
ISSN:2223-1536
Container-title:Proceedings of the Southwest State University. Series: IT Management, Computer Science, Computer Engineering. Medical Equipment Engineering
language:
Short-container-title:jour

Author:

Kuzmin A. A.¹,Tomakova R. A.¹,Petrunina Е. V.²,Ermakov D. А.¹,Kadyrova S.¹

Affiliation:

1. Southwest State University

2. Moscow Polytechnic University

Abstract

The purpose of research is development of a method for controlling muscle fatigue in robotic devices operating in a combined mode.Methods. To calculate the exogenous moment of forces of a robotic device, a surface electromyosignal decoder is proposed, which takes into account the effect of the operator's muscle fatigue. By decoding the electromyosignal, the assisting torque on the servomotors of the robotic device is determined. When calculating the assisting moment, the degree of muscle fatigue is taken into account. The method for assessing muscle fatigue consists in assessing the indicator of synchronism of electromyosignals on synergistic muscles and is based on a hybrid approach to the formation of a decision-making module. The first decision-making module is built on the basis of a neural network classifier, the descriptors for which are formed based on the analysis of the spectra of electromyosignals of synergistic muscles. The second decision module includes two synergy channels per electromyographic channel. The first synergy channel is obtained by amplitude demodulation of the electromyosignal, and the second - by its frequency demodulation. As a result, we obtain two muscle fatigue classifiers, the solutions of which are integrated by the aggregator.Results. Experimental studies of the dependence of the electromyosignal on the magnitude of muscle effort and its duration were carried out, which showed that the relative change in the average RMS index under static load can serve as an objective indicator of the degree of muscle fatigue.Conclusion. The developed method makes it possible to control the mechanical moments on the servomotors of a robotic device adequately to the test muscle load and the functional state of the user's muscles. The method allows for individual adjustment of the neural network classifier block and the fuzzy inference block with subsequent aggregation of their solutions and thus optimize the combined operation mode of the robotic device.

Publisher

Southwest State University

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