Muscle-tendon model with length history-dependent activation-velocity coupling

Area of Science:

• Biomechanical Engineering
• Muscle Physiology
• Computational Neuroscience

Background:

• Understanding muscle mechanics is crucial for biomechanics and neuroscience.
• Existing models often lack accuracy in predicting complex muscle behaviors like length-dependent coupling.

Purpose of the Study:

• To develop and validate a novel muscle-tendon model.
• To incorporate length-dependent coupling between activation and velocity.
• To evaluate the model's predictive capabilities in electrically stimulated cat soleus muscles.

Main Methods:

• Developed muscle-tendon models with Hill-type structure and length-dependent activation-velocity coupling.
• Estimated dynamic parameters using nonlinear parameter estimation from simultaneous random stimulation and length changes.
• Estimated static parameters from the length-tension curve.
• Validated the model against experimental data from cat soleus muscles.

Main Results:

• The developed model accurately predicted muscle behavior under diverse conditions, including random perturbations and isovelocity movements.
• The model successfully captured short-range stiffness and length history-dependent post-yielding behavior.
• The model demonstrated predictive accuracy for twitch responses.
• A fixed-parameter model with length history-dependent activation-velocity coupling showed high generality.

Conclusions:

• The novel muscle-tendon model provides a robust framework for simulating muscle dynamics.
• The incorporation of length-dependent activation-velocity coupling enhances predictive accuracy.
• The model's generality makes it suitable for applications in simulation and feedforward control where online adaptation is not feasible.