Lower Cervical Spine Motion Segment Computational Model Validation: Kinematic and Kinetic Response for Quasi-Static and Dynamic Loading.


Journal Article

Advanced computational human body models (HBM) enabling enhanced safety require verification and validation at different levels or scales. Specifically, the motion segments, which are the building blocks of a detailed neck model, must be validated with representative experimental data to have confidence in segment and, ultimately, full neck model response. In this study, we introduce detailed finite element motion segment models and assess the models for quasi-static and dynamic loading scenarios. Finite element segment models at all levels in the lower human cervical spine were developed from scans of a 26-yr old male subject. Material properties were derived from the in vitro experimental data. The segment models were simulated in quasi-static loading in flexion, extension, lateral bending and axial rotation, and at dynamic rates in flexion and extension in comparison to previous experimental studies and new dynamic experimental data introduced in this study. Single-valued experimental data did not provide adequate information to assess the model biofidelity, while application of traditional corridor methods highlighted that data sets with higher variability could lead to an incorrect conclusion of improved model biofidelity. Data sets with continuous or multiple moment-rotation measurements enabled the use of cross-correlation for an objective assessment of the model and highlighted the importance of assessing all motion segments of the lower cervical spine to evaluate the model biofidelity. The presented new segment models of the lower cervical spine, assessed for range of motion and dynamic/traumatic loading scenarios, provide a foundation to construct a biofidelic model of the spine and neck, which can be used to understand and mitigate injury for improved human safety in the future.

Full Text

Duke Authors

Cited Authors

  • Barker, JB; Cronin, DS; Nightingale, RW

Published Date

  • June 2017

Published In

Volume / Issue

  • 139 / 6

PubMed ID

  • 28418508

Pubmed Central ID

  • 28418508

Electronic International Standard Serial Number (EISSN)

  • 1528-8951

International Standard Serial Number (ISSN)

  • 0148-0731

Digital Object Identifier (DOI)

  • 10.1115/1.4036464


  • eng