Motor control complexity can be dynamically simplified during gait pattern exploration using motor control-based biofeedback

Alyssa M. Spomer, Robin Z. Yan, Michael H. Schwartz, Katherine M. Steele

Research output: Contribution to journalArticlepeer-review


Understanding how the central nervous system coordinates diverse motor outputs has been a topic of extensive investigation. Although it is generally accepted that a small set of synergies underlies many common activities, such as walking, whether synergies are equally robust across a broader array of gait patterns or can be flexibly modified remains unclear. Here, we evaluated the extent to which synergies changed as nondisabled adults (n = 14) explored gait patterns using custom biofeedback. Secondarily we used Bayesian additive regression trees to identify factors that were associated with synergy modulation. Participants, explored 41.1±8.0 gait patterns using biofeedback, during which synergy recruitment changed depending on the type and magnitude of gait pattern modification. Specifically, a consistent set of synergies was recruited to accommodate small deviations from baseline but additional synergies emerged for larger gait changes. Synergy complexity was similarly modulated; complexity decreased for 82 6% of the attempted gait patterns, but distal gait mechanics were strongly associated with these changes In particular greater ankle dorsiflexion moments and knee flexion through stance, as well as greater knee extension moments at initial contact corresponded to a reduction in synergy complexity. Taken together, these results suggest that the central nervous system preferentially adopts a low-dimensional, largely Invariant control strategy but can modify that strategy to produce diverse gait patterns Beyond improving understanding of how synergies are recruited during gait, study outcomes may also help identify parameters that can be targeted with interventions to alter synergies and Improve motor control after neurological injury.

Original languageEnglish (US)
Pages (from-to)984-998
Number of pages15
JournalJournal of neurophysiology
Issue number5
StatePublished - May 2023

Bibliographical note

Publisher Copyright:
Copyright © 2023 the American Physiological Society.


  • Bayesian additive regression trees
  • biofeedback
  • electromyography
  • locomotion
  • muscle synergies

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.


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