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Hybrid musculoskeletal model-based 3D asymmetric lifting prediction and comparison with symmetric lifting.

Yujiang XiangRahid ZamanAsif ArefeenJoel QuarnstromRitwik RakshitJames Yang
Published in: Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine (2023)
In this study, a 3D asymmetric lifting motion is predicted by using a hybrid predictive model to prevent potential musculoskeletal lower back injuries for asymmetric lifting tasks. The hybrid model has two modules: a skeletal module and an OpenSim musculoskeletal module. The skeletal module consists of a dynamic joint strength based 40 degrees of freedom spatial skeletal model. The skeletal module can predict the lifting motion, ground reaction forces (GRFs), and center of pressure (COP) trajectory using an inverse dynamics-based motion optimization method. The musculoskeletal module consists of a 324-muscle-actuated full-body lumbar spine model. Based on the predicted kinematics, GRFs and COP data from the skeletal module, the musculoskeletal module estimates muscle activations using static optimization and joint reaction forces through the joint reaction analysis tool in OpenSim. The predicted asymmetric motion and GRFs are validated with experimental data. Muscle activation results between the simulated and experimental EMG are also compared to validate the model. Finally, the shear and compression spine loads are compared to NIOSH recommended limits. The differences between asymmetric and symmetric liftings are also compared.
Keyphrases
  • skeletal muscle
  • solid state
  • high speed
  • big data
  • working memory
  • network analysis