一种基于表面肌电信号的腕部肌力估计方法研究

doi:10.11896/jsjkx.200600021

Abstract

Abstract: Human-machine interaction force control based on surface electromyography (sEMG) needs to detect the force of muscle,and it is very difficult to measure muscle force directly and accurately.Therefore,muscle force estimation method is often used to estimate muscle force.A method for estimating wrist muscle force with sEMG signals is proposed.This method first makes a muscle force acquisition platform,then collects a series of muscle force signals and sEMG signals of wristat different muscle force levels,filters and matches the two signals synchronously,and takes the root mean square,mean absolute value (MAV).The mean frequency and spectral moments ratio (SMR) of the sEMG signal are taken as the fourfeatures.Finally,Support vector machine (SVM) modeling is used to achieve muscle force estimation and compared with the BP neural network modeling results.The root mean square error of the muscle force estimation of two experimenters reaches 9.1% MVC (maximum isometric contraction force) and 8.7% MVC,respectively.The results show that the method in this paper is an effective and simple method for estimating wrist muscle force.

Key words: BP neural network, Muscle force estimation, Support vector machine, Surface EMG signal, Wrist

CLC Number:

TP249

GUO Fu-min, ZHANG Hua, HU Rong-hua, SONG Yan. Study on Method for Estimating Wrist Muscle Force Based on Surface EMG Signals[J].Computer Science, 2021, 48(6A): 317-320.

References

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Study on Method for Estimating Wrist Muscle Force Based on Surface EMG Signals

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