Development of a shoulder muscle feedback controller for human body models.

Area of Science:

• Biomechanics
• Computational modeling
• Human body modeling

Background:

• Finite element human body models (FE HBMs) are crucial for simulating occupant kinematics during pre-crash maneuvers like braking and steering.
• Accurate modeling of the shoulder complex, with its high degree of mobility and reliance on muscle activity for stability, is essential for driver FE HBMs.
• Understanding intermuscular load sharing is vital for realistic shoulder complex simulations, as stability is maintained through co-contraction, not just muscle geometry.

Purpose of the Study:

• To implement a novel shoulder muscle controller with directionally dependent intermuscular load sharing into an FE HBM.
• To validate the new controller's performance by comparing its predictions to experimental data from human volunteers.
• To assess the controller's ability to reproduce occupant kinematics under dynamic loading conditions.

Main Methods:

• Developed a new shoulder muscle controller incorporating intermuscular load sharing based on volunteer muscle activity data.
• Simulated a volunteer experiment involving dynamic elbow loading in eight different directions to evaluate the controller.
• Conducted a sensitivity study to optimize controller gains for improved accuracy.

Main Results:

• The FE HBM with the new shoulder muscle controller successfully predicted peak elbow displacements across all tested loading directions.
• Model predictions showed good agreement with volunteer kinematics, validating the controller's effectiveness.
• Sensitivity analysis provided insights into controller gain optimization.

Conclusions:

• The developed shoulder muscle controller accurately simulates elbow kinematics under dynamic loading.
• The controller's ability to incorporate directionally dependent intermuscular load sharing enhances the realism of FE HBMs.
• This validated controller is ready for integration into full-body active FE HBMs for advanced driver maneuver simulations.