Semiparametric Identification of Human Arm Dynamics for Flexible Control of a Functional Electrical Stimulation Neuroprosthesis

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

14 Scopus citations


We present a method to identify the dynamics of a human arm controlled by an implanted functional electrical stimulation neuroprosthesis. The method uses Gaussian process regression to predict shoulder and elbow torques given the shoulder and elbow joint positions and velocities and the electrical stimulation inputs to muscles. We compare the accuracy of torque predictions of nonparametric, semiparametric, and parametric model types. The most accurate of the three model types is a semiparametric Gaussian process model that combines the flexibility of a black box function approximator with the generalization power of a parameterized model. The semiparametric model predicted torques during stimulation of multiple muscles with errors less than 20% of the total muscle torque and passive torque needed to drive the arm. The identified model allows us to define an arbitrary reaching trajectory and approximately determine the muscle stimulations required to drive the arm along that trajectory.

Original languageEnglish (US)
Article number7422110
Pages (from-to)1405-1415
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number12
StatePublished - Dec 2016


  • Electrical stimulation
  • neural prosthesis
  • neuromuscular stimulation
  • statistical learning
  • system identification

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering


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