T. Nadler, S. Wolfen, D. Häufle, and S. Schmitt. Dataset, (2024)Related to: Driess, D., Zimmermann, H., Wolfen, S., Suissa, D., Haeufle, D., Hennes, D., Toussaint, M. & Schmitt, S. (2018, May). Learning to control redundant musculoskeletal systems with neural networks and SQP: exploiting muscle properties. In 2018 IEEE International Conference on robotics and automation (ICRA) (pp. 6461-6468). IEEE. doi: 10.1109/ICRA.2018.8463160.
P. Santana Chacon, M. Hammer, I. Wochner, J. Walter, and S. Schmitt. Software, (2023)Related to: P. F. S. Chacon, M. Hammer, I. Wochner, J. R. Walter and S. Schmitt. A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers. doi: 10.1080/10255842.2023.2293652.
L. Schmid, T. Klotz, O. Röhrle, R. Powers, F. Negro, and U. Yavuz. Software, (2023)Related to: Schmid L, Klotz T, Röhrle O, Powers RK, Negro F, et al. (2024) Postinhibitory excitation in motoneurons can be facilitated by hyperpolarization-activated inward currents: A simulation study. PLOS Computational Biology 20(1): e1011487. doi: 10.1371/journal.pcbi.1011487.
P. Lerge, L. Nölle, and S. Schmitt. Dataset, (2023)Related to: Lerge P., Nölle L.V., Schmitt S., Deep learning-based estimation of the neutral body posture considering three-dimensional local joint angles, Human Factors 2023.