Mathematical models of human paralyzed muscle after long-term training

J Biomech, 2007 · DOI: 10.1016/j.jbiomech.2006.12.015 · Published: January 1, 2007

Spinal Cord Injury Neurorehabilitation Biomechanics

Simple Explanation

Spinal cord injury (SCI) leads to changes in muscles, including atrophy and increased fatigue. Functional electrical stimulation (FES) can help prevent these changes. The study explores how well mathematical models can predict muscle force in paralyzed muscles after long-term FES training. Three models (linear and two nonlinear) were tested to predict force in the trained soleus muscle. The models were also compared between trained and untrained limbs to see the impact of training. The study found that nonlinear models were more accurate in predicting muscle force properties in both trained and untrained paralyzed muscles. The model parameters also changed with training, showing the models responded to the muscle's condition.

Study Duration

Not specified

Participants

4 males with complete SCI

Evidence Level

Not specified

Key Findings

1
Nonlinear models, especially the Hill Huxley type, predict paralyzed muscle force properties more accurately than linear models.
2
Model parameter values are sensitive to the physiological state of the paralyzed muscle, changing with training.
3
The linear model had difficulty predicting peak force and force time integral with higher frequency and/or dual doublet conditions.

Research Summary

This study compared three mathematical muscle models (linear, 2nd order nonlinear, and Hill Huxley type nonlinear) for predicting force properties in trained and untrained paralyzed soleus muscles of individuals with spinal cord injury. The main finding was that nonlinear models, particularly the Hill Huxley type, more accurately predicted muscle force compared to the linear model. The models were sensitive to training-induced changes in muscle properties, as reflected in parameter value adjustments. The study suggests that while simpler models could be integrated into control strategies for neuro-prosthetic devices, the Hill Huxley type model might be preferred when specific force-time properties like half-relaxation time are critical.

Practical Implications

Neuroprosthetic Design

Nonlinear models can improve the accuracy of neuroprosthetic device control algorithms.

Personalized FES

Model parameters should be adjusted based on the individual's training status to optimize FES outcomes.

Rehabilitation Strategies

Understanding muscle adaptations through modeling can inform more effective training programs for individuals with SCI.

Study Limitations

1
The small sample size may limit the generalizability of the findings to different training regimens.
2
The study used only one parameterization input train, potentially influencing the optimized model.
3
Variability between subjects and stimulation patterns poses challenges for model comparisons and practical implementation.

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