Does joint impedance improve dynamic leg simulations with explicit and implicit solvers?

Area of Science:

• Biomechanics
• Computational modeling
• Neurorehabilitation

Background:

• Traumatic injuries like stroke disrupt neural control, affecting both kinematic and kinetic aspects of human motion.
• Accurate biomechanical models are crucial for diagnosing mobility impairments, but real-time, subject-specific simulations require optimization.
• Current diagnostic tools may miss subtle dynamic variables in patient movement.

Approach:

• A 17-degrees of freedom (DOF) bipedal model with intrinsic viscoelastic elements was developed.
• The study evaluated the impact of viscoelasticity, numerical integration methods, and sampling frequency on simulation accuracy and stability.
• Numerical errors were assessed in dynamic simulations using swing-phase experimental kinematics.

Key Points:

• Optimal selection of viscoelasticity, integration method, and sampling rate achieved accurate joint kinematics (<1% error) and kinetics (<5% error).
• Joint viscoelasticity significantly reduced integration errors for explicit numerical methods.
• Implicit numerical methods showed minimal benefit from added joint viscoelasticity.

Conclusions:

• Simulation optimization enhances the accuracy of biomechanical models for diagnosing mobility issues.
• Findings can improve diagnostic tools and real-time feedback simulations for neuromuscular disease recovery.
• Insights support the development of intuitive control for prosthetic solutions.