Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 317-320.doi: 10.11896/jsjkx.200600021

• Intelligent Computing • Previous Articles     Next Articles

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

GUO Fu-min, ZHANG Hua, HU Rong-hua, SONG Yan   

  1. Robot & Welding Automation,Nanchang 330031,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:GUO Fu-min,born in 1992,doctoral student.His main research interests include exoskelton robot and so on.
    ZHANG Hua,born in 1964,Ph.D,professor,Ph.D supervisor.His current research interests include welding automation techniques,and smart control.
  • Supported by:
    Advantageous Science and Technology Innovation Team Construction Plan Project of Jiangxi Province,China(20171BCB24001).

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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
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