Literature DB >> 33186886

Negotiating ground level perturbations in walking: Visual perception and expectation of curb height modulate muscle activity.

Roy Müller1, Johanna Vielemeyer2, Daniel F B Häufle3.   

Abstract

To negotiate visible and unpredictable changes in ground level, humans use different control strategies depending on the visibility. In case of fully visible perturbations, humans can anticipate the occurrence and the magnitude of the perturbation. In case of a camouflaged perturbation, they can anticipate the occurrence based on the camouflage cover but need to predict the magnitude from experience, as it is not visible. The purpose of this study was to investigate the anticipatory muscular control strategy humans employ when walking down curbs of different height and to investigate how this strategy differs if the step down is fully visible or camouflaged. The activity of five bilateral lower limb muscles (M. gastrocnemius medialis, M. soleus, M. tibialis anterior, M. biceps femoris and M. vastus medialis) of eight healthy subjects was recorded during walking down visible (0, -10 and -20 cm) and camouflaged curbs (0 and -10 cm). The results reveal that the M. gastrocnemius shows a clear anticipatory adaptation to visible curbs in the contralateral and partly also the ipsilateral leg, which further depends on the curb height. Furthermore, in case of a camouflaged perturbation, M. gastrocnemius activity of the contralateral leg shows an adaptation that indicates an average prediction of the curb height, presumably based on previous experience.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Anticipation; EMG; Feed-forward; Feedback; Gastrocnemius

Year:  2020        PMID: 33186886     DOI: 10.1016/j.jbiomech.2020.110121

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  2 in total

1.  Evaluating anticipatory control strategies for their capability to cope with step-down perturbations in computer simulations of human walking.

Authors:  Lucas Schreff; Daniel F B Haeufle; Johanna Vielemeyer; Roy Müller
Journal:  Sci Rep       Date:  2022-06-16       Impact factor: 4.996

Review 2.  Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation.

Authors:  Seungmoon Song; Łukasz Kidziński; Xue Bin Peng; Carmichael Ong; Jennifer Hicks; Sergey Levine; Christopher G Atkeson; Scott L Delp
Journal:  J Neuroeng Rehabil       Date:  2021-08-16       Impact factor: 4.262

  2 in total

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