Musculoskeletal simulations play a crucial role in rehabilitation, orthopedic implant design and athletic performance enhancement. A computational challenge within these simulations involves efficiently estimating muscle-tendon lengths and moment arms, especially in multijoint, multidegree-of-freedom (DoF) systems. When modeling muscles spanning joints with six DoFs, the required number of terms can significantly increase, which could compromise computational speed. This study introduces a method that significantly reduces the polynomial coefficients needed for muscle-tendon length and moment arm parametrization, ensuring computational efficiency without compromising accuracy. The approach was applied with two different error thresholds and was validated across four gait movements recorded from an elderly subject with a knee prosthesis, using data from the dataset of the Grand Challenge to predict in vivo contact forces. The results indicate that this reduction strategy decreased the required polynomial coefficients by approximately 50%, particularly for muscles spanning the knee and ankle joints, while preserving high accuracy in joint angle and knee contact force tracking. We demonstrate that our method decreases the computation time required for simulating full-body dynamics by 15.6%, estimating knee contact pressures including a knee joint with all six DoFs. We observed minimal differences between the optimal solutions obtained using the full and reduced polynomials. This approach offers a simplified and computationally efficient method for muscle-driven simulations, making it more practical for clinical applications like in physiotherapy, robotic-assisted surgery, and athletic training. By increasing computational speed without losing accuracy, this method marks a notable advance in musculoskeletal modeling.
Harba et al. (Sun,) studied this question.