Literature DB >> 21938651

Comparison of electromyography and force as interfaces for prosthetic control.

Elaine A Corbett1, Eric J Perreault, Todd A Kuiken.   

Abstract

The ease with which persons with upper-limb amputations can control their powered prostheses is largely determined by the efficacy of the user command interface. One needs to understand the abilities of the human operator regarding the different available options. Electromyography (EMG) is widely used to control powered upper-limb prostheses. It is an indirect estimator of muscle force and may be expected to limit the control capabilities of the prosthesis user. This study compared EMG control with force control, an interface that is used in everyday interactions with the environment. We used both methods to perform a position-tracking task. Direct-position control of the wrist provided an upper bound for human-operator capabilities. The results demonstrated that an EMG control interface is as effective as force control for the position-tracking task. We also examined the effects of gain and tracking frequency on EMG control to explore the limits of this control interface. We found that information transmission rates for myoelectric control were best at higher tracking frequencies than at the frequencies previously reported for position control. The results may be useful for the design of prostheses and prosthetic controllers.

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Mesh:

Year:  2011        PMID: 21938651      PMCID: PMC4316207          DOI: 10.1682/jrrd.2010.03.0028

Source DB:  PubMed          Journal:  J Rehabil Res Dev        ISSN: 0748-7711


  8 in total

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  8 in total
  10 in total

1.  Decoding Intent With Control Theory: Comparing Muscle Versus Manual Interface Performance.

Authors:  Momona Yamagami; Katherine M Steele; Samuel A Burden
Journal:  Proc SIGCHI Conf Hum Factor Comput Syst       Date:  2020-04-23

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Authors:  Craig Ades; Iker Gonzalez; Mostapha AlSaidi; Mehrdad Nojoumian; Ou Bai; Aparna Aravelli; Leonel Lagos; Erik D Engeberg
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5.  The effect of prosthetic feedback on the strategies and synergies used by vestibular loss subjects to control stance.

Authors:  Flurin Honegger; Imke M A Hillebrandt; Nadja G A van den Elzen; Kok-Sing Tang; John H J Allum
Journal:  J Neuroeng Rehabil       Date:  2013-12-19       Impact factor: 4.262

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Journal:  Front Neurorobot       Date:  2016-08-22       Impact factor: 2.650

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Authors:  Xiao Hu; Daniel Ludvig; Wendy M Murray; Eric J Perreault
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9.  Leveraging Joint Mechanics Simplifies the Neural Control of Movement.

Authors:  Daniel Ludvig; Mariah W Whitmore; Eric J Perreault
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Journal:  J Neuroeng Rehabil       Date:  2014-04-19       Impact factor: 4.262

  10 in total

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