Musculotendon lengths and moment arms for a three-dimensional upper-extremity model

Area of Science:

• Biomechanics
• Computational modeling
• Human movement analysis

Background:

• Musculoskeletal models are crucial for simulating human movement.
• Calculating musculotendon geometry is computationally expensive, hindering simulation efficiency.
• Upper-extremity models present unique challenges due to complex muscle paths and multi-articular muscles.

Purpose of the Study:

• To develop computationally efficient polynomial regression equations for estimating upper-extremity musculotendon length and moment arms.
• To reduce the computational cost associated with generating forward dynamics simulations of human movement.

Main Methods:

• Developed polynomial regression equations using a least squares fitting technique.
• Utilized geometry data from a validated public-domain upper-extremity musculoskeletal model.
• Estimated musculotendon length and moment arms for 32 upper-extremity musculotendon actuators.

Main Results:

• Regression equations demonstrated a good fit to original model values (average RMSE of 0.39 mm).
• Computational time for simulating wheelchair propulsion was reduced by over two orders of magnitude (315 s to 2.3 s).
• The developed equations accurately estimate musculotendon geometry for shoulder, elbow, and wrist joints.

Conclusions:

• The polynomial regression equations provide a computationally efficient method for estimating upper-extremity musculotendon geometry.
• These equations can significantly accelerate the generation of forward dynamics simulations for various upper-extremity movements.
• This approach facilitates more accessible and widespread use of detailed musculoskeletal models in research and clinical applications.