Sensitivity of muscle force estimations to changes in muscle input parameters using nonlinear optimization approaches

Area of Science:

• Biomechanics
• Musculoskeletal modeling
• Computational anatomy

Background:

• Accurate muscle force calculation is crucial for understanding human movement and diagnosing musculoskeletal disorders.
• Existing models often rely on optimization techniques to determine force distribution between synergistic muscles.
• Sensitivity of these models to input parameter variations remains a critical concern.

Purpose of the Study:

• To investigate the sensitivity of muscle force calculations to variations in muscle input parameters.
• To quantify the impact of anatomical variations on predicted muscle forces.
• To compare the influence of input parameters versus optimization methods on force calculation accuracy.

Main Methods:

• Analytical derivation of force sharing for two synergistic muscles in a one-degree-of-freedom system.
• Application of three distinct nonlinear optimization approaches.
• Systematic variation of muscle input parameters within normal anatomical ranges.

Main Results:

• Changes in input parameters, even within typical anatomical variations, frequently resulted in muscle force calculation changes exceeding 100%.
• The choice of objective and constraint functions in optimization approaches showed a lesser impact compared to input parameter accuracy.
• Demonstrated significant sensitivity of muscle force predictions to the quality of input data.

Conclusions:

• Inadequate muscle input parameters pose a substantial source of error in muscle force calculations.
• The accuracy of biomechanical models is heavily reliant on precise anatomical and physiological input data.
• Future research should prioritize refining methods for obtaining accurate muscle input parameters to improve the reliability of musculoskeletal models.