Date of Degree
PhD (Doctor of Philosophy)
Study Design: Computational analyses using optimization finite element (FE) models.
Objective: To determine the spinal muscle forces (MFs) creating compressive follower loads (CFL) in the lumbar spine in various sagittal postures and to investigate if such MFs can maintain the spinal stability.
Summary of Background Data: Biomechanical loads are known closely associated with spinal disorders. Normal spinal loads, however, remains poorly understood due to the lack of knowledge of the MF control mechanism for normal biomechanical functions.
Methods: 3-D optimization and FE models of the spinal system (trunk, lumbar spine, sacrum, pelvis, and 232 muscles) were developed and validated using reported experimental data. Optimization models were used to determine the MFs creating CFLs in the lumbar spine in various sagittal postures from 10 extension to 40 flexion. The deformation of the lumbar spine under these MFs and trunk weight was predicted from FE models. The stable lumbar spine deformation was determined by the resultant trunk sway < 10 mm.
Results: Optimization solutions of MFs, CFLs, and follower load path (FLP) location were feasible for all studied postures. The FE predictions clearly demonstrated that MFs creating CFLs along the base spinal curve connecting the geometrical centers or along a curve in its vicinity (within anterior or posterior shift by 2 mm) produce the stable deformation of the lumbar spine in the neutral standing and flexed postures, whereas the MFs creating the smallest CFLs resulted in the unstable deformation. In case of extended postures, however, it was not possible to find the CFL creating MFs that produce stable deformation of the extended spine.
Conclusion: The results of this study demonstrated the feasibility for spinal muscles to stabilize the spine via the CFL mechanism.
3-D FE, follower load, muscle, muscle control mechanism, otimization, spine
x, 128 pages
Includes bibliographical references (pages 102-108).
Copyright 2011 Byeong sam Kim