Characterising Spinal Load-Displacement Behaviour Using Representative Components

Description

In this presentation delivered by Samantha Hayward, key insights from her PhD research on spinal load displacement behavior are discussed. The presentation begins with an acknowledgment of the global issue of lower back pain, which remains inadequately understood despite decades of study. Hayward critiques the traditional 6x6 stiffness matrix method for characterizing spinal behavior, noting its limitations in capturing complex features such as non-linearity, stiffening, and hysteresis.



She introduces viscoelastic models as an alternative to describe the biological materials of the spine, emphasizing a novel approach that analytically decomposes experimental data to validate the use of spring and damper elements. The methodology involves testing porcine lumbar spine specimens under cyclic loading and applying polynomial fits to segregate spring and damping contributions to spinal behavior.



The data, analyzed for parameters including axial torsion and anterior-posterior shear, highlights significant improvements in fit quality when using viscoelastic modeling over classical methods, revealing decreased root mean square errors. Hayward concludes with the assertion that the complex mechanical behavior of the spine can be effectively characterized using a combination of non-linear hardening springs and viscous damping, thus providing a better understanding of spinal dynamics and informing future research and treatment approaches for back pain.

DOI: 10.1302/3114-240493

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