The optimization-based dynamic prediction of 3D human running motion is studied in this paper. A predictive dynamics method is used to formulate the running problem, and normal running is formulated as a symmetric and cyclic motion. Recursive Lagrangian dynamics with analytical gradients for all the constraints and objective function are incorporated in the optimization process. The dynamic effort is used as the performance measure, and the impulse at the foot strike is also included in the performance measure. The joint angle profiles and joint torque profiles are calculated for the full-body human model, and the ground reaction force (GRF) is determined. Several cause-and-effect cases are studied, and the formulation for upper-body yawing motion is proposed and simulated. Simulation results from this methodology show good correlation with experimental data obtained from human subjects and the existing literature.
Dynamic Optimization of Human Running With Analytical Gradients
Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received September 25, 2013; final manuscript received May 9, 2014; published online January 12, 2015. Assoc. Editor: Jozsef Kovecses.
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Chung, H., Arora, J. S., Abdel-Malek, K., and Xiang, Y. (March 1, 2015). "Dynamic Optimization of Human Running With Analytical Gradients." ASME. J. Comput. Nonlinear Dynam. March 2015; 10(2): 021006. https://doi.org/10.1115/1.4027672
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