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Evidence for a Time-Invariant Phase Variable in Human Ankle Control

Overview of attention for article published in PLOS ONE, February 2014
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Title
Evidence for a Time-Invariant Phase Variable in Human Ankle Control
Published in
PLOS ONE, February 2014
DOI 10.1371/journal.pone.0089163
Pubmed ID
Authors

Robert D. Gregg, Elliott J. Rouse, Levi J. Hargrove, Jonathon W. Sensinger

Abstract

Human locomotion is a rhythmic task in which patterns of muscle activity are modulated by state-dependent feedback to accommodate perturbations. Two popular theories have been proposed for the underlying embodiment of phase in the human pattern generator: a time-dependent internal representation or a time-invariant feedback representation (i.e., reflex mechanisms). In either case the neuromuscular system must update or represent the phase of locomotor patterns based on the system state, which can include measurements of hundreds of variables. However, a much simpler representation of phase has emerged in recent designs for legged robots, which control joint patterns as functions of a single monotonic mechanical variable, termed a phase variable. We propose that human joint patterns may similarly depend on a physical phase variable, specifically the heel-to-toe movement of the Center of Pressure under the foot. We found that when the ankle is unexpectedly rotated to a position it would have encountered later in the step, the Center of Pressure also shifts forward to the corresponding later position, and the remaining portion of the gait pattern ensues. This phase shift suggests that the progression of the stance ankle is controlled by a biomechanical phase variable, motivating future investigations of phase variables in human locomotor control.

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Mendeley readers

The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Italy 1 <1%
Slovenia 1 <1%
Brazil 1 <1%
Unknown 115 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 29 24%
Student > Ph. D. Student 26 22%
Researcher 14 12%
Student > Doctoral Student 10 8%
Professor > Associate Professor 8 7%
Other 16 13%
Unknown 16 13%
Readers by discipline Count As %
Engineering 76 64%
Agricultural and Biological Sciences 4 3%
Sports and Recreations 3 3%
Medicine and Dentistry 3 3%
Computer Science 3 3%
Other 10 8%
Unknown 20 17%