Reducing Complexity in Muscle-Tendon Kinematics Parameterization Improves Convergence Speed in Musculoskeletal...

Area of Science:

• Biomechanics
• Computational modeling
• Musculoskeletal system

Background:

• Musculoskeletal simulations are vital for rehabilitation, implant design, and athletic performance.
• Estimating muscle-tendon lengths and moment arms is computationally intensive, especially in complex multi-joint systems.
• Modeling muscles with six degrees-of-freedom (DoF) increases complexity and can slow simulations.

Purpose of the Study:

• To introduce a computationally efficient method for parametrizing muscle-tendon lengths and moment arms.
• To reduce the number of polynomial coefficients required in musculoskeletal models.
• To maintain accuracy while improving the speed of full-body dynamic simulations.

Main Methods:

• Developed a strategy to significantly reduce polynomial coefficients for muscle-tendon properties.
• Validated the method using data from four gait movements of an elderly subject with a knee prosthesis.
• Applied two different error thresholds and analyzed results for muscles spanning the knee and ankle.

Main Results:

• Reduced required polynomial coefficients by approximately 50% for knee and ankle spanning muscles.
• Decreased computation time for full-body dynamics simulations by 15.6%.
• Achieved high accuracy in joint angle and knee contact force tracking with minimal differences from full polynomial solutions.

Conclusions:

• The proposed method offers a computationally efficient and accurate approach for muscle-driven simulations.
• This simplification is practical for clinical applications in physiotherapy, robotic surgery, and athletic training.
• The advancement improves the speed of complex musculoskeletal modeling without compromising precision.