Multi-muscle FES force control of the human arm for arbitrary goals

Eric M. Schearer, Yu Wei Liao, Eric J. Perreault, Matthew C. Tresch, William D. Memberg, Robert F. Kirsch, Kevin M. Lynch

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


We present a method for controlling a neuroprosthesis for a paralyzed human arm using functional electrical stimulation (FES) and characterize the errors of the controller. The subject has surgically implanted electrodes for stimulating muscles in her shoulder and arm. Using input/output data, a model mapping muscle stimulations to isometric endpoint forces measured at the subject's hand was identified. We inverted the model of this redundant and coupled multiple-input multiple-output system by minimizing muscle activations and used this inverse for feedforward control. The magnitude of the total root mean square error over a grid in the volume of achievable isometric endpoint force targets was 11% of the total range of achievable forces. Major sources of error were random error due to trial-to-trial variability and model bias due to nonstationary system properties. Because the muscles working collectively are the actuators of the skeletal system, the quantification of errors in force control guides designs of motion controllers for multi-joint, multi-muscle FES systems that can achieve arbitrary goals.

Original languageEnglish (US)
Article number6623194
Pages (from-to)654-663
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number3
StatePublished - May 2014


  • Force control
  • neural prosthesis
  • neuromuscular stimulation
  • system identification

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering


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