A biologically-assisted curved muscle model of the lumbar spine: Model structure. (August 2016)

• Record Type: Journal Article
• Authors: Hwang, Jaejin; Knapik, Gregory G.; Dufour, Jonathan S.; Aurand, Alexander; Best, Thomas M.; Khan, Safdar N.; Mendel, Ehud; Marras, William S.
• Is Part Of: Clinical biomechanics. Volume 37(2016)
• Page Start: 53
• Page End: 59
• Publication Date: 2016-08
• Subjects: Curved muscle -- Personalization -- Biomechanical model -- Model structure -- Spine; Biomechanics -- Periodicals; Osteopathic medicine -- Periodicals; Biomechanics -- Periodicals; Osteopathic Medicine -- Periodicals; 612.76
• DOI: 10.1016/j.clinbiomech.2016.06.002
• Languages: English
• ISSNs: 0268-0033

Abstract

Abstract: Background: Biomechanical models have been developed to assess the spine tissue loads of individuals. However, most models have assumed trunk muscle lines of action as straight-lines, which might be less reliable during occupational tasks that require complex lumbar motions. The objective of this study was to describe the model structure and underlying logic of a biologically-assisted curved muscle model of the lumbar spine. Methods: The developed model structure including curved muscle geometry, separation of active and passive muscle forces, and personalization of muscle properties was described. An example of the model procedure including data collection, personalization, and data evaluation was also illustrated. Findings: Three-dimensional curved muscle geometry was developed based on a predictive model using magnetic resonance imaging and anthropometric measures to personalize the model for each individual. Calibration algorithms were able to reverse-engineer personalized muscle properties to calculate active and passive muscle forces of each individual. Interpretation: This biologically-assisted curved muscle model will significantly increase the accuracy of spinal tissue load predictions for the entire lumbar spine during complex dynamic occupational tasks. Personalized active and passive muscle force algorithms will help to more robustly investigate person-specific muscle forces and spinal tissue loads. Highlights: The structure of a biologically-assisted curved muscle model was described. Muscle personalization was accounted for estimating active and passive forces. This model provides person-specific spinal loads during complex task performed.