A model-based approach to predict muscle synergies using optimization: Application to feedback control

Reza Sharif Razavian, Naser Mehrabi, John McPhee

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

This paper presents a new model-based method to define muscle synergies. Unlike the conventional factorization approach, which extracts synergies from electromyographic data, the proposed method employs a biomechanical model and formally defines the synergies as the solution of an optimal control problem. As a result, the number of required synergies is directly related to the dimensions of the operational space. The estimated synergies are posture-dependent, which correlate well with the results of standard factorization methods. Two examples are used to showcase this method: a two-dimensional forearm model, and a three-dimensional driver arm model. It has been shown here that the synergies need to be task-specific (i.e., they are defined for the specific operational spaces: the elbow angle and the steering wheel angle in the two systems). This functional definition of synergies results in a low-dimensional control space, in which every force in the operational space is accurately created by a unique combination of synergies. As such, there is no need for extra criteria (e.g., minimizing effort) in the process of motion control. This approach is motivated by the need for fast and bio-plausible feedback control of musculoskeletal systems, and can have important implications in engineering, motor control, and biomechanics.

Original languageEnglish (US)
Article number121
JournalFrontiers in Computational Neuroscience
Volume9
Issue numberOCT
DOIs
StatePublished - Oct 6 2015
Externally publishedYes

Keywords

  • Dynamic redundancy
  • Model-based approach
  • Muscle synergy
  • Operational space
  • Optimization
  • Real-time control
  • Task-specific
  • Unique solution

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Cellular and Molecular Neuroscience

Fingerprint

Dive into the research topics of 'A model-based approach to predict muscle synergies using optimization: Application to feedback control'. Together they form a unique fingerprint.

Cite this