Muscle synergies facilitate computational prediction of subject-specific walking motions

Andrew J. Meyer, Ilan Eskinazi, Jennifer N. Jackson, Anil V. Rao, Carolynn Patten, Benjamin J. Fregly

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations.

Original languageEnglish (US)
Article number77
JournalFrontiers in Bioengineering and Biotechnology
Volume4
Issue numberOCT
DOIs
StatePublished - Jan 1 2016
Externally publishedYes

Fingerprint

Walking
Muscle
Muscles
Biomechanical Phenomena
Gait
Kinematics
Leg
Stroke
Kinetics
Chemical activation
Aptitude
Body Image
Recovery of Function
Torque
Electromyography
Exercise equipment
Calibration
Torque control
Foot
Joints

Keywords

  • Biomechanics
  • Computational neurorehabilitation
  • Direct collocation optimal control
  • Muscle synergy analysis
  • Neuromusculoskeletal modeling
  • Predictive gait optimization

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Histology
  • Biomedical Engineering

Cite this

Muscle synergies facilitate computational prediction of subject-specific walking motions. / Meyer, Andrew J.; Eskinazi, Ilan; Jackson, Jennifer N.; Rao, Anil V.; Patten, Carolynn; Fregly, Benjamin J.

In: Frontiers in Bioengineering and Biotechnology, Vol. 4, No. OCT, 77, 01.01.2016.

Research output: Contribution to journalArticle

Meyer, Andrew J. ; Eskinazi, Ilan ; Jackson, Jennifer N. ; Rao, Anil V. ; Patten, Carolynn ; Fregly, Benjamin J. / Muscle synergies facilitate computational prediction of subject-specific walking motions. In: Frontiers in Bioengineering and Biotechnology. 2016 ; Vol. 4, No. OCT.
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