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Validation Method for Motion Segments of the Lower Cervical Spine: Kinematic and Kinetic Response for Quasi-static and Dynamic Loading

[+] Author and Article Information
Jeffrey B. Barker

Department of Mechatronics and Mechanical Engineering, University of Waterloo 200 University Ave. West, Waterloo, Ontario, Canada, N2L 3G1
jbarker@uwaterloo.ca

Duane S. Cronin

Department of Mechatronics and Mechanical Engineering, University of Waterloo 200 University Ave. West, Waterloo, Ontario, Canada, N2L 3G1
dscronin@uwaterloo.ca

Roger W. Nightingale

Duke University, Department of Biomedical Engineering, Division of Orthopaedic Surgery Box 90281, Durham, NC 27708-0281, USA
rwn@duke.edu

1Corresponding author.

ASME doi:10.1115/1.4036464 History: Received October 31, 2016; Revised March 17, 2017

Abstract

Advanced computational human body models enabling enhanced occupant 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 demonstrate the importance of objective validation for quasi-static and dynamic loading. Finite element segment models at all levels in the lower human cervical spine were developed from scans of a 26 year old male subject. Material properties were derived from in vitro experimental testing 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. 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 multiple moment-rotation measurements enabled the use of cross-correlation for an objective evaluation of the model with respect to the data. Such an objective validation method is beneficial for assessing the biofidelity of computational models, with the limitation of distinguishing the magnitude but not the orientation of differences. It is recommended that cross-correlation be used to compare motion segment models to individual tests, where these results can be averaged for all test cases and spinal levels to provide an assessment of model biofidelity.

Copyright (c) 2017 by ASME
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