We developed a biomechanical digital human model (DHM) simulation framework that uses (synergetic) Hill type muscles as actuators and optimal control (OC) for motion generation. In this work, we start investigating the underlying actuation signals of the Hill type muscles. We have set up a weight lifting test (‘biceps curls’) in the motion lab, where we measure the muscle activation via electromyography (EMG). The via muscles actuated simulation model produces human like trajectories for different types of OC cost functions, whereas the underlying muscle actuations strongly differ from each other. Our first results indicate that a muscle synergy actuation is more robust concerning the variation of activation signals and that a specific mix of cost functions preserves the resulting motion behavior while producing more human like actuation signals.