Predicting Human Chronically Paralyzed Muscle Force: a Comparison of Three Mathematical Models.
From: Graduate Program in Physical Therapy and Rehabilitation Science, 1-252 Medical Education Bldg., The Univ. of Iowa, Iowa City, IA 52242, USA.
Journal of applied physiology (Bethesda, Md. : 1985)
- Publish Date: Mar 2006
- ISSN: 8750-7587
- Volume: 100
- Issue: 3
- Pages: 1027-36
- Medium: Print
- Language: English
- Citation (JAMA): Frey Law Laura A, Shields Richard K, et al. Predicting Human Chronically Paralyzed Muscle Force: a Comparison of Three Mathematical Models.. J. Appl. Physiol. Mar 2006;100:1027-36
Abstract
Chronic spinal cord injury (SCI) induces detrimental musculoskeletal adaptations that adversely affect health status, ranging from muscle paralysis and skin ulcerations to osteoporosis. SCI rehabilitative efforts may increasingly focus on preserving the integrity of paralyzed extremities to maximize health quality using electrical stimulation for isometric training and/or functional activities. Subject-specific mathematical muscle models could prove valuable for predicting the forces necessary to achieve therapeutic loading conditions in individuals with paralyzed limbs. Although numerous muscle models are available, three modeling approaches were chosen that can accommodate a variety of stimulation input patterns. To our knowledge, no direct comparisons between models using paralyzed muscle have been reported. The three models include 1) a simple second-order linear model with three parameters and 2) two six-parameter nonlinear models (a second-order nonlinear model and a Hill-derived nonlinear model). Soleus muscle forces from four individuals with complete, chronic SCI were used to optimize each model’s parameters (using an increasing and decreasing frequency ramp) and to assess the models’ predictive accuracies for constant and variable (doublet) stimulation trains at 5, 10, and 20 Hz in each individual. Despite the large differences in modeling approaches, the mean predicted force errors differed only moderately (8-15% error; P=0.0042), suggesting physiological force can be adequately represented by multiple mathematical constructs. The two nonlinear models predicted specific force characteristics better than the linear model in nearly all stimulation conditions, with minimal differences between the two nonlinear models. Either nonlinear mathematical model can provide reasonable force estimates; individual application needs may dictate the preferred modeling strategy.
Mesh Headings (Keywords): Adult, Biomechanics, Chronic Disease, Data Interpretation, Statistical, Electric Stimulation, Electromyography, Humans, Linear Models, Male, Mathematics, Models, Biological, Muscle Contraction, Muscle, Skeletal, Nonlinear Dynamics, Paralysis, Predictive Value of Tests, Reproducibility of Results, Spinal Cord Injuries
Check for Full Text / PubMed Unique Identifier (PMID): 16306255
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