Physically correlated muscle activation for a human head and neck computational model.

Area of Science:

• Biomechanics
• Computational Modeling
• Human Anatomy

Background:

• Accurate head and neck biomechanical modeling is crucial for understanding injury mechanisms.
• Existing models often simplify muscle activation dynamics, potentially limiting simulation fidelity.

Purpose of the Study:

• To develop and optimize a computational model of the head and neck complex.
• To analyze the impact of muscle activation parameters on head kinematics during flexion and extension.
• To correlate computational predictions with physical experimental data.

Main Methods:

• Utilized a 50th percentile male head and neck complex model.
• Incorporated 15 muscle pairs using the Hill Muscle Model within MADYMO.
• Performed sensitivity analysis and numerical optimization for muscle activation timing and rates.
• Correlated model outputs with physical experimental data.

Main Results:

• Muscle activation level and timing were identified as key parameters influencing system kinematics.
• Optimized activation patterns showed a 9% correlation with measured values during initial head flexion.
• Activation onset occurred at 90 ms, consistent with known muscle reaction times.
• Found that continuous activation/deactivation rates, not binary (0% or 100%), are essential for accurate simulations.

Conclusions:

• The study highlights the critical role of nuanced muscle activation parameters in head and neck biomechanical simulations.
• Optimized activation timing and rates improve the correlation between computational models and physical data.
• Simplified binary muscle activation can lead to inaccurate simulation outcomes.