Literature DB >> 2227975

Restoring unassisted natural gait to paraplegics via functional neuromuscular stimulation: a computer simulation study.

G T Yamaguchi1, F E Zajac.   

Abstract

Functional neuromuscular stimulation (FNS) of paralyzed muscles has enabled spinal-cord-injured patients to regain a semblance of lower-extremity control, for example to ambulate while relying heavily on the use of walkers. Given the limitations of FNS, specifically low muscle strengths, high rates of fatigue, and a limited ability to modulate muscle excitations, it remains unclear, however, whether FNS can be developed as a practical means to control the lower extremity musculature to restore aesthetic, unsupported gait to paraplegics. A computer simulation of FNS-assisted bipedal gait shows that it is difficult, but possible to attain undisturbed, level gait at normal speeds provided the electrically-stimulated ankle plantarflexors exhibit either near-normal strengths or are augmented by an orthosis, and at least seven muscle-groups in each leg are stimulated. A combination of dynamic programming and an open-loop, trial-and-error adjustment process was used to find a suboptimal set of discretely-varying muscle stimulation patterns needed for a 3-D, 8 degree-of-freedom dynamic model to sustain a step. An ankle-foot orthosis was found to be especially useful, as it helped to stabilize the stance leg and simplified the task of controlling the foot during swing. It is believed that the process of simulating natural gait with this model will serve to highlight difficulties to be expected during laboratory and clinical trials.

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Year:  1990        PMID: 2227975     DOI: 10.1109/10.58599

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  20 in total

1.  Simple and complex models for studying muscle function in walking.

Authors:  Marcus G Pandy
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2003-09-29       Impact factor: 6.237

2.  Contributions of muscles and passive dynamics to swing initiation over a range of walking speeds.

Authors:  Melanie D Fox; Scott L Delp
Journal:  J Biomech       Date:  2010-03-16       Impact factor: 2.712

3.  A three-dimensional model of the rat hindlimb: musculoskeletal geometry and muscle moment arms.

Authors:  Will L Johnson; Devin L Jindrich; Roland R Roy; V Reggie Edgerton
Journal:  J Biomech       Date:  2007-12-03       Impact factor: 2.712

4.  Control of FES-induced cyclical movements of the lower leg.

Authors:  P H Veltink
Journal:  Med Biol Eng Comput       Date:  1991-11       Impact factor: 2.602

5.  Morphometry of the human thigh muscles. A comparison between anatomical sections and computer tomographic and magnetic resonance images.

Authors:  C M Engstrom; G E Loeb; J G Reid; W J Forrest; L Avruch
Journal:  J Anat       Date:  1991-06       Impact factor: 2.610

6.  Interlimb coordination in body-weight supported locomotion: A pilot study.

Authors:  Stefan Seiterle; Tyler Susko; Panagiotis K Artemiadis; Robert Riener; Hermano Igo Krebs
Journal:  J Biomech       Date:  2015-05-08       Impact factor: 2.712

7.  Reconstructing muscle activation during normal walking: a comparison of symbolic and connectionist machine learning techniques.

Authors:  B W Heller; P H Veltink; N J Rijkhoff; W L Rutten; B J Andrews
Journal:  Biol Cybern       Date:  1993       Impact factor: 2.086

8.  Cycle-to-cycle control of swing phase of paraplegic gait induced by surface electrical stimulation.

Authors:  H M Franken; P H Veltink; G Baardman; R A Redmeyer; H B Boom
Journal:  Med Biol Eng Comput       Date:  1995-05       Impact factor: 2.602

9.  Model-based development of neuroprosthesis for paraplegic patients.

Authors:  R Riener
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  1999-05-29       Impact factor: 6.237

10.  A dual-learning paradigm can simultaneously train multiple characteristics of walking.

Authors:  Matthew A Statton; Alexis Toliver; Amy J Bastian
Journal:  J Neurophysiol       Date:  2016-03-09       Impact factor: 2.714

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