Robot-Driven Locomotor Perturbations Reveal Synergy- Mediated, Context-Dependent Feedforward and Feedback Mechanisms of Adaptation

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Title: Robot-Driven Locomotor Perturbations Reveal Synergy- Mediated, Context-Dependent Feedforward and Feedback Mechanisms of Adaptation
Authors: Severini, GiacomoKoenig, AlexanderAdans-Dester, Catherineet al.
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Date: 25-Mar-2020
Online since: 2021-03-04T16:20:01Z
Abstract: Humans respond to mechanical perturbations that affect their gait by changing their motor control strategy. Previous work indicates that adaptation during gait is context dependent, and perturbations altering long-term stability are compensated for even at the cost of higher energy expenditure. However, it is unclear if gait adaptation is driven by unilateral or bilateral mechanisms, and what the roles of feedback and feedforward control are in the generation of compensatory responses. Here, we used a robot-based adaptation paradigm to investigate if feedback/feedforward and unilateral/bilateral contributions to locomotor adaptation are also context dependent in healthy adults. A robot was used to induce two opposite unilateral mechanical perturbations affecting the step length over multiple gait cycles. Electromyographic signals were collected and analyzed to determine how muscle synergies change in response to perturbations. The results unraveled different unilateral modulation dynamics of the muscle-synergy activations during adaptation, characterized by the combination of a slow-progressive feedforward process and a fast-reactive feedback-driven process. The relative unilateral contributions of the two processes to motor-output adjustments, however, depended on which perturbation was delivered. Overall, these observations provide evidence that, in humans, both descending and afferent drives project onto the same spinal interneuronal networks that encode locomotor muscle synergies.
Funding Details: Enterprise Ireland
European Commission Horizon 2020
Science Foundation Ireland
Funding Details: Insight Research Centre
German Federal Ministry of Research and Education
Bavarian Ministry of Economic Affairs
European Space Agency
German Federal Ministry of Economic Affairs and Energy
National Institutes of Health (NIH)
Lemelson Foundation
Hong Kong Research Grants Council
Chinese University of Hong Kong
Guangdong Provincial Hospital of Traditional Chinese Medicine
Golden King
Shun Hing Institute of Advanced Engineering
American Heart Association
Department of Defense
Michael J Fox Foundation
National Science Foundation
Peabody Foundation
Barrett Technology
Emerge Diagnostics
Mitsui Chemicals
Shimmer Research
Type of material: Journal Article
Publisher: Springer Nature
Journal:  Scientific Reports
Volume: 10
Copyright (published version): 2020 the Authors
Keywords: Personal sensingBiophysical modelsCentral patterns generators
DOI: 10.1038/s41598-020-61231-8
Language: en
Status of Item: Peer reviewed
This item is made available under a Creative Commons License:
Appears in Collections:Electrical and Electronic Engineering Research Collection
Insight Research Collection

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