Optimization‐based dynamic human walking prediction: One step formulation

A new methodology is introduced in this work to simulate normal walking using a spatial digital human model. The proposed methodology is based on an optimization formulation that minimizes the dynamic effort of people during walking while considering associated physical and kinematical constraints. Normal walking 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. Dynamic balance of the model is enforced by direct use of the equations of motion. In addition, the ground reaction forces are calculated using a new algorithm that enforces overall equilibrium of the human skeletal model. External loads on the human body, such as backpacks, are also included in the formulation. Simulation results with the present methodology show good correlation with the experimental data obtained from human subjects and the existing literature.