Applying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity...

Area of Science:

• Biomechanics
• Computational modeling
• Human physiology

Background:

• Existing mathematical models for muscle force, fatigue, and musculoskeletal systems are numerous but not well integrated for high-intensity exercise.
• Accurate subject-specific modeling of multi-level muscle behavior presents significant challenges.

Purpose of the Study:

• To adapt the three-compartment controller (3CCr) muscle fatigue model for an inverse-dynamics optimization algorithm.
• To model elbow flexion/extension moments for both isometric and dynamic (bicep curl) exercises.
• To investigate subject-specific modeling challenges and introduce a novel fiber-type recruitment criterion.

Main Methods:

• Adapted the three-compartment controller (3CCr) model with an inverse-dynamics optimization algorithm for 7 elbow muscles.
• Simulated muscle activity and rest periods for isometric and dynamic bicep curl tasks.
• Incorporated fiber-type recruitment hierarchy into the optimization for novel muscle modeling.

Main Results:

• Static (isometric) cases yielded more accurate torque predictions than dynamic cases.
• Dynamic cases required more precise muscle parameter calibration to predict sufficient torque.
• Model modifications were necessary to prevent unrealistic constant fatigue in high-intensity exercise-rest cycles.

Conclusions:

• Integrating diverse muscle models for high-intensity exercise is complex, especially for subject-specific accuracy.
• The proposed fiber-type recruitment criterion shows promise for enhanced muscle force modeling.
• Further refinement in calibration and consideration of factors like muscular potentiation are needed for dynamic exercise modeling.