Investigation of lumbar spine biomechanics using global convergence optimization and constant loading path methods

Won Man Park; Young Joon Kim; Shaobai Wang; Yoon Hyuk Kim; Guoan Li; Won Man Park; Young Joon Kim; Shaobai Wang; Yoon Hyuk Kim; Guoan Li

doi:10.3934/mbe.2020168

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 4: 2970-2983. doi: 10.3934/mbe.2020168

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Investigation of lumbar spine biomechanics using global convergence optimization and constant loading path methods

1.
Elsoltec, Yongin-si, Gyeonggi-do 16950, South Korea
2.
Columbia College, Columbia University, New York, NY 10027, USA
3.
Key Laboratory of Exercise and Health Sciences of Ministry of Education, Shanghai University of Sport, Shanghai 20444, China
4.
Department of Mechanical Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
5.
Orthopaedic Bioengineering Research Center, Newton-Wellesley Hospital and Harvard Medical School, Newton, MA 02462, USA

Received: 30 January 2019 Accepted: 10 March 2020 Published: 31 March 2020

Computational models and inverse dynamic optimization methods are used to predict in-vivo spinal loading. Spinal force is conventionally predicted using the constant loading path method, which is based on the concept that the physiological directions of the spine loads follow the same path of the spinal curve. However, the global convergence optimization method, in which the instantaneous center of rotation of the joint should be also predicted, is necessary for accurate prediction of joint forces of the human body. In this study, we investigate the joint forces, instantaneous centers of rotation, and muscle forces of the human lumbar spine using both global convergence optimization method and constant loading path method during flexion, upright standing, and extension postures. The joint forces predicted using the constant loading path method were 130%, 234%, and 253% greater than those predicted using the global convergence optimization method for the three postures. The instantaneous centers of rotation predicted using the global convergence optimization method were segment level-dependent and moved anteriorly in the flexion and posteriorly in the extension, whereas those predicted using the constant loading path method moved posteriorly in both the flexion and extension. The data indicated that compared to the global convergence optimization method, the constant loading path method introduces additional constraints to the spinal joint model, and thus, it results in greater joint and muscle forces.
- spine biomechanics,
- lumbar spine,
- inverse dynamics,
- global convergence optimization method,
- constant loading path
Citation: Won Man Park, Young Joon Kim, Shaobai Wang, Yoon Hyuk Kim, Guoan Li. Investigation of lumbar spine biomechanics using global convergence optimization and constant loading path methods[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 2970-2983. doi: 10.3934/mbe.2020168

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Abstract

Computational models and inverse dynamic optimization methods are used to predict in-vivo spinal loading. Spinal force is conventionally predicted using the constant loading path method, which is based on the concept that the physiological directions of the spine loads follow the same path of the spinal curve. However, the global convergence optimization method, in which the instantaneous center of rotation of the joint should be also predicted, is necessary for accurate prediction of joint forces of the human body. In this study, we investigate the joint forces, instantaneous centers of rotation, and muscle forces of the human lumbar spine using both global convergence optimization method and constant loading path method during flexion, upright standing, and extension postures. The joint forces predicted using the constant loading path method were 130%, 234%, and 253% greater than those predicted using the global convergence optimization method for the three postures. The instantaneous centers of rotation predicted using the global convergence optimization method were segment level-dependent and moved anteriorly in the flexion and posteriorly in the extension, whereas those predicted using the constant loading path method moved posteriorly in both the flexion and extension. The data indicated that compared to the global convergence optimization method, the constant loading path method introduces additional constraints to the spinal joint model, and thus, it results in greater joint and muscle forces.

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