Sparse optimal motor estimation (SOME) for extracting commands for prosthetic limbs

Yao Li*, Lauren H. Smith, Levi J. Hargrove, Douglas J. Weber, Gerald E. Loeb

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

It is possible to replace amputated limbs with mechatronic prostheses, but their operation requires the user's intentions to be detected and converted into control signals to the actuators. Fortunately, the motoneurons (MNs) that controlled the amputated muscles remain intact and capable of generating electrical signals, but these signals are difficult to record. Even the latest microelectrode array technologies and targeted motor reinnervation can provide only sparse sampling of the hundreds of motor units that comprise the motor pool for each muscle. Simple rectification and integration of such records is likely to produce noisy and delayed estimates of the actual intentions of the user. We have developed a novel algorithm for optimal estimation of motor pool excitation based on the recruitment and firing rates of a small number (2-10) of discriminated motor units. We first derived the motor estimation algorithm from normal patterns of modulated MN activity based on a previously published model of individual MN recruitment and asynchronous frequency modulation. The algorithm was then validated on a target motor reinnervation subject using intramuscular fine-wire recordings to obtain single motor units.

Original languageEnglish (US)
Article number6313919
Pages (from-to)104-111
Number of pages8
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume21
Issue number1
DOIs
StatePublished - Jan 16 2013

Keywords

  • Motor neuron pool
  • sparse estimation
  • targeted motor reinnervation

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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