In this study, a hybrid predictive model is used to predict 3D asymmetric lifting motion and assess 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 optimization method. The equations of motion are built by recursive Lagrangian dynamics. The musculoskeletal module consists of a 324-muscle-actuated full-body lumbar spine model. Based on the generated 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. Muscle activation results between simulated and experimental EMG are compared to validate the model. Finally, potential lower back injuries are evaluated for a specific-weight asymmetric lifting task. The shear and compression spine loads are compared to NIOSH recommended limits. At the beginning of the dynamic lifting process, the simulated compressive spine load beyond the NIOSH action limit but less than the permissible limit. This is due to the fatigue factors considered in NIOSH lifting equation.
Trajectory optimization with musculoskeletal models can be used to reconstruct measured movements and to predict changes in movements in response to environmental changes. It enables an exhaustive analysis of joint angles, joint moments, ground reaction forces, and muscle forces, among others. However, its application is still limited to simplified problems in two dimensional space or straight motions. The simulation of movements with directional changes, e.g. curved running, requires detailed three dimensional models which lead to a high-dimensional solution space. We extended a full-body three dimensional musculoskeletal model to be specialized for running with directional changes. Model dynamics were implemented implicitly and trajectory optimization problems were solved with direct collocation to enable efficient computation. Standing, straight running, and curved running were simulated starting from a random initial guess to confirm the capabilities of our model and approach: efficacy, tracking and predictive power. Altogether the simulations required 1 h 17 min and corresponded well to the reference data. The prediction of curved running using straight running as tracking data revealed the necessity of avoiding interpenetration of body segments. In summary, the proposed formulation is able to efficiently predict a new motion task while preserving dynamic consistency. Hence, labor-intensive and thus costly experimental studies could be replaced by simulations for movement analysis and virtual product design.
more » « less- NSF-PAR ID:
- 10198419
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
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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.
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