Mathematical models of human paralyzed muscle after long-term training.

Mathematical models of human paralyzed muscle after long-term training. Research Abstract Details 

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Mathematical models of human paralyzed muscle after long-term training. Abstract Text:

L A Frey Law,R K Shields,

Spinal cord injury (SCI) results in major musculoskeletal adaptations, including muscle atrophy, faster contractile properties, increased fatigability, and bone loss. The use of functional electrical stimulation (FES) provides a method to prevent paralyzed muscle adaptations in order to sustain force-generating capacity. Mathematical muscle models may be able to predict optimal activation strategies during FES, however muscle properties further adapt with long-term training. The purpose of this study was to compare the accuracy of three muscle models, one linear and two nonlinear, for predicting paralyzed soleus muscle force after exposure to long-term FES training. Further, we contrasted the findings between the trained and untrained limbs. The three models' parameters were best fit to a single force train in the trained soleus muscle (N=4). Nine additional force trains (test trains) were predicted for each subject using the developed models. Model errors between predicted and experimental force trains were determined, including specific muscle force properties. The mean overall error was greatest for the linear model (15.8%) and least for the nonlinear Hill Huxley type model (7.8%). No significant error differences were observed between the trained versus untrained limbs, although model parameter values were significantly altered with training. This study confirmed that nonlinear models most accurately predict both trained and untrained paralyzed muscle force properties. Moreover, the optimized model parameter values were responsive to the relative physiological state of the paralyzed muscle (trained versus untrained). These findings are relevant for the design and control of neuro-prosthetic devices for those with SCI.

Mathematical models of human paralyzed muscle after long-term training. Publishing Authors By Initials

LA Law,RK Shields,

For similar natural sciences: time: time factors research abstracts see: natural sciences: time: time factors research

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Mathematical models of human paralyzed muscle after long-term training. Journal Published:

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: Journal of biomechanics

VOLUME: 40

Page Numbers: 2587-95

Journal Abbreviation:

ISSN: 0021-9290

DAY: 20

MONTH: 02

YEAR: 2007

Mathematical models of human paralyzed muscle after long-term training. Information

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LANGUAGE: eng

NlmUniqueID: 157375

Mathematical models of human paralyzed muscle after long-term training. Keywords Mesh Terms:

KEYWORDS: Time Factors

MESH TERMS: physiopathology

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Grant and Affiliation Information for Mathematical models of human paralyzed muscle after long-term training.

AFFILIATION: Graduate Program in Physical Therapy & Rehabilitation Science, The University of Iowa, 1-252 Medical Education Building, Iowa City, IA 52242-1190, USA.

Country: United States

AGENCY: United States NINR

GRANT: R01-NR010285

ACRONYM: NR

MEDLINETA: J Biomech

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