On data-guided optimal control simulation of human motion

This work investigates the combination of optical motion capturing data with optimal control simulations of human motion, which can be important in a wide range of applications in the professional as well as the private sector, ranging from health and ergonomics over human-machine-interaction to sports and games 1–3]. There are methodically very different approaches to include optical measurement data in the simulation of human motion, see e.g. 4–6]. Two different approaches to combine data and simulation are investigated in this work. Either we use a soft constraints approach, where the difference of simulated and measured marker positions is part of the objective function (1), or we formulate an hard constraints approach with nonlinear constraints that set an upper bound on this difference (2), while the objective function is purely physiologically motivated. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)