Inverse estimation of multiple muscle activations from joint moment with muscle synergy extraction

Zhan Li, David Guiraud, Mitsuhiro Hayashibe

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

Human movement is produced resulting from synergetic combinations of multiple muscle contractions. The resultant joint movement can be estimated through the related multiple-muscle activities, which is formulated as the forward problem. Neuroprosthetic applications may benefit from cocontraction of agonist and antagonist muscle pairs to achieve more stable and robust joint movements. It is necessary to estimate the activation of each individual muscle from desired joint torque(s), which is the inverse problem. A synergy-based solution is presented for the inverse estimation of multiple muscle activations from joint movement, focusing on one degree-of-freedom tasks. The approach comprises muscle synergy extraction via the nonnegative matrix factorization algorithm. Cross validation is performed to evaluate the method for prediction accuracy based on experimental data from ten able-bodied subjects. The results demonstrate that the approach succeeds to inversely estimate the multiple muscle activities from the given joint torque sequence. In addition, the other one's averaged synergy ratio was applied for muscle activation estimation with leave-one-out cross-validation manner, which resulted in 9.3% estimation error over all the subjects. The obtained results support the common muscle synergy-based neuroprosthetics control concept.

Original languageEnglish
Article number6862826
Pages (from-to)64-73
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume19
Issue number1
DOIs
Publication statusPublished - 2015 Jan 1
Externally publishedYes

Keywords

  • Electromyography (EMG)
  • inverse problem
  • muscle activations
  • muscle synergy

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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