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Multi-Channel EMG Processing

  • Edward A. Clancy
  • William R. Murray
  • Neville Hogan

Abstract

A review of some recent progress in improving the fidelity of electromyogram-based estimates of muscle mechanical activity is presented. Initially, theoretical techniques and experimental investigations into electromyogram amplitude estimation are described. A stochastic functional model of the surface electromyogram, which incorporates single channel optimization (temporal whitening) and spatial combination of multiple channels, is described. Compared to standard electromyogram amplitude estimators, experimental investigations confirm that the model-based estimators improve the amplitude estimate by as much as a factor of 3–4. These experimental investigations also suggest the need for a new functional electromyogram model which includes an additive noise source. Sensitivities of the temporal whitening and spatial combination algorithms are reported.

Because muscles are not commonly used in isolation, it is necessary to model both agonist and antagonist muscles about a joint in order to relate the electromyogram to joint torque and stiffness. A simple model of the elbow joint, which incorporates co-contraction, is reviewed. The model predicts that co-contraction often leads to antagonist muscle activation levels that are significantly larger than the corresponding level of agonist activation, that is, muscles supporting the limb against gravity will not work as hard as their antagonist counterparts in the presence of significant co-contraction. Experimental observations confirm this prediction. This same agonist/antagonist muscle model then is used to relate the surface electromyogram to joint torque. Simulation studies that investigated the co-contraction model demonstrate that robust joint torque estimation could be accomplished in the presence of muscular co-contraction. In an experimental trial, the higher fidelity electromyogram amplitude estimators produced higher fidelity electromyogram-to-torque processors.

Keywords

Maximum Voluntary Contraction Joint Torque Antagonist Muscle Myoelectric Signal Shaping Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Edward A. Clancy
    • 1
  • William R. Murray
    • 2
  • Neville Hogan
    • 3
  1. 1.Liberty Mutual Research Center for Safety and HealthHopkintonUSA
  2. 2.Department of Mechanical EngineeringUniversity of WashingtonSeattleUSA
  3. 3.Department of Mechanical Engineering and Department of Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridgeUSA

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