Using uniaxial pseudorandom stress stimuli to develop soft tissue constitutive equations.

Area of Science:

• Biomedical Engineering
• Materials Science
• Biomechanics

Background:

• Soft tissues exhibit complex nonlinear and viscoelastic behaviors.
• Developing accurate constitutive models for soft tissues is crucial for understanding their mechanical properties and for applications in medical device design and surgical simulation.

Purpose of the Study:

• To develop a nonlinear systems identification method for creating constitutive equations for soft tissue specimens.
• To validate the predictive capability of these equations for various stress inputs.

Main Methods:

• A pseudorandom Gaussian (PGN) stress input was applied to soft tissue specimens (rat medial collateral knee ligaments and rat skin) under uniaxial tension.
• Volterra-Wiener kernels (first and second order) were calculated from the measured strain response.
• The developed constitutive equations were used to predict strain responses to sinusoidal stress inputs.

Main Results:

• The method successfully developed constitutive equations for rat medial collateral knee ligaments and rat skin.
• Predicted strains showed good agreement with measured strains for sinusoidal stress inputs, with normalized mean squared errors (NMSE) between 0.01-0.08.
• Prediction accuracy was maintained when applied stress magnitudes were within the range of the original PGN input.

Conclusions:

• The nonlinear systems identification method effectively models soft tissue behavior.
• The developed constitutive equations can predict both nonlinear and viscoelastic responses across a range of stress inputs.
• This approach offers a robust method for characterizing soft tissue mechanics.