Hybrid Method for Controlling Muscle Fatigue in the Robotic System

A. A. Kuzmin, R. A. Tomakova, Е. V. Petrunina, D. А. Ermakov, S. Kadyrova
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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.
控制机器人系统肌肉疲劳的混合方法
研究目的是开发一种方法,用于控制以组合模式运行的机器人设备的肌肉疲劳。为了计算机器人装置的外力力矩,提出了一种表面肌电信号解码器,该解码器考虑了操作员肌肉疲劳的影响。通过解码肌电信号,可以确定机器人装置伺服电机的辅助力矩。在计算辅助力矩时,要考虑到肌肉疲劳程度。评估肌肉疲劳的方法包括评估协同肌肉上肌电信号的同步性指标,并基于混合方法形成决策模块。第一个决策模块是在神经网络分类器的基础上建立的,其描述符是在分析协同肌肉肌电信号频谱的基础上形成的。第二个决策模块包括每个肌电通道的两个协同通道。第一个协同通道通过肌电信号的振幅解调获得,第二个协同通道通过频率解调获得。因此,我们获得了两个肌肉疲劳分类器,其解决方案由聚合器整合。实验研究了肌电信号对肌肉用力程度和持续时间的依赖性,结果表明,静态负荷下平均有效值指数的相对变化可作为肌肉疲劳程度的客观指标。所开发的方法可以根据测试肌肉负荷和使用者肌肉的功能状态,适当控制机器人设备伺服电机上的机械力矩。该方法允许对神经网络分类器和模糊推理块进行单独调整,然后汇总它们的解决方案,从而优化机器人设备的综合运行模式。
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