On optical data-guided optimal control simulations of human motion

Abstract

This work addresses the synergistic fusion of optimal control simulations and marker-based optical measurements of human motion. The latter is a widespread capturing technology in biomechanics and movement science. In the context of optimal control simulations, the idea is to improve the computational performance by using a realistic initial guess and to increase the realism of the simulated motion through data-guiding. In the context of motion capturing, the idea is to use biomechanical simulations in order to maintain accurate capturings also with reduced measurement frequencies and points. This would greatly improve the usability of such systems in terms of setup time and wearing comfort. In this work, we investigate different methods for combining physical laws, 3D marker positions obtained from the optical system, and physiologically motivated objectives in an optimal control framework. Moreover, we explore the potential of obtaining reasonable results—in terms of motion trajectories and torques that are close to reference obtained from using all available information—with a reduced measurement frequency and a reduced number of markers. The tests are performed on a human steering and throwing motion, where a human arm was captured with seven retroreflective markers at $120\text{ Hz}$. Our results show, that a significant reduction of exploited measurements still provides feasible simulation results in our proposed method, given that the physiologically motivated objective reflects the actual movement. Furthermore, it turns out that neglecting markers close to the shoulder has less influence on the simulation results than neglecting markers close to the hand.