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The Development of a Personalized Curved Muscle Model Applicable to Complex Dynamic Exertion
Jaejin Hwang
出版
Ohio State University
, 2016
URL
http://books.google.com.hk/books?id=vWnPswEACAAJ&hl=&source=gbs_api
註釋
Accurate estimates of trunk muscle geometry including the muscle line of action are essential to the development of biologically-assisted models capable of accurately documenting spinal tissue loads. Currently most biomechanical models of the spine employ straight-line muscle approximations for computational efficiency. However, this straight line of action assumption is thought to violate the physiological reality of the lumbar spine and would provide a wrong estimate of spine tissue loading especially during asymmetric complex exertions. The incorporation of muscle curvature within the biomechanical model has been suggested as a means to more accurately predict spine tissue loads. However, the approach to defining the curved geometry and line of action of the muscle for modeling purposes and the logic associated with integrating this information into current biologically-assisted models have not yet been addressed. Hence, the objective of this study was to develop personalized curved muscle geometry and integrate this information into a biologically-assisted spine model using an individual's anthropometric measures, trunk kinematics, kinetics, and muscle activities. Four major steps were conducted to meet this goal. First, a systematic literature review was performed and 35 studies met the selection criteria. The most common technique of curved muscle path was the "via-point" method. Curved muscle geometry was commonly developed from MRI/CT database and cadaveric dissections, and inverse dynamics models were typically used to estimate muscle forces. Several models have attempted to examine their results by comparing their approach with previous studies, however, only limited conditions were able to be examined. Second, based on magnetic resonance imaging data from thirty subjects (10 male and 20 female) reported in a previous study, a polynomial regression analysis was conducted to estimate the muscle moment-arms and physiological cross-sectional areas through thoracic/lumbar spine as a function of vertebral level, gender, age, height, and body mass. Third, the model structure was developed to include curved muscle geometry, separation of active and passive muscle forces, and personalization of muscle properties. Lastly, model fidelity of the curved muscle model through the entire lumbar spine was evaluated for 24 subjects (13 males and 11 females) during a wide range of complex dynamic lifting exertions. In general, curved muscle model predicted at least 80% of the variability in spinal moments, and less than 15% of average moment matching error across levels. The compression and anterior-posterior shear load significantly increased as trunk bent more, whereas the lateral shear load significantly increased as trunk twisted more asymmetric during lifting tasks. The results of this study indicate that a curved muscle representation in the biologically-assisted model is an empirically reasonable approach to accurately estimate spinal moments and spinal tissue loads of the lumbar spine. This advancement is expected to help quantify risk exposure more accurately, improve patient health and enable the development of interventions that could mitigate risks in occupational jobs