Literature DB >> 19201619

Determination of ankle muscle power in normal gait using an EMG-to-force processing approach.

R A Bogey1, A J Gitter, L A Barnes.   

Abstract

The purpose of this study was to determine the contribution of individual ankle muscles to the net ankle power and to examine each muscle's role in propulsion or support of the body during normal, self-selected-speed walking. An EMG-to-force processing (EFP) model was developed which scaled muscle tendon unit force output to gait EMG, with that muscle's power output being the product of muscle force and contraction velocity. Net EFP power was determined by summing individual ankle muscle power. Net ankle power was also calculated for these subjects via inverse dynamics. Closeness of fit of the power curves of the two methods was used to validate the model. The curves were highly correlated (r(2)=.91), thus the model was deconstructed to analyze the power contribution and role of each ankle muscle during normal gait. Key findings were that the plantar flexors control tibial rotation in single support, and act to propel the entire limb into swing phase. The dorsiflexors provide positive power for swing phase foot clearance, negative power to control early stance phase foot placement, and a second positive power burst to actively advance the tibia in the transition from double to single support. Co-contraction of agonists and antagonists was limited to only a small percentage of the gait cycle.

Mesh:

Year:  2010        PMID: 19201619     DOI: 10.1016/j.jelekin.2008.09.013

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  5 in total

1.  The impact of dynamic balance measures on walking performance in multiple sclerosis.

Authors:  Nora E Fritz; Rhul Evans R Marasigan; Peter A Calabresi; Scott D Newsome; Kathleen M Zackowski
Journal:  Neurorehabil Neural Repair       Date:  2014-05-01       Impact factor: 3.919

2.  Quantitative sensory and motor measures detect change overtime and correlate with walking speed in individuals with multiple sclerosis.

Authors:  Kathleen M Zackowski; Joseph I Wang; John McGready; Peter A Calabresi; Scott D Newsome
Journal:  Mult Scler Relat Disord       Date:  2015-01       Impact factor: 4.339

3.  Building Effective Machine Learning Models for Ankle Joint Power Estimation During Walking Using FMG Sensors.

Authors:  Oliver Heeb; Arnab Barua; Carlo Menon; Xianta Jiang
Journal:  Front Neurorobot       Date:  2022-04-01       Impact factor: 2.650

4.  The functional role of the triceps surae muscle during human locomotion.

Authors:  Jean-Louis Honeine; Marco Schieppati; Olivier Gagey; Manh-Cuong Do
Journal:  PLoS One       Date:  2013-01-16       Impact factor: 3.240

5.  Estimates of individual muscle power production in normal adult walking.

Authors:  Ross A Bogey; Lee A Barnes
Journal:  J Neuroeng Rehabil       Date:  2017-09-11       Impact factor: 4.262

  5 in total

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