People can perform everyday movements such as gripping a drinking glass seemingly effortlessly. If this ability is impaired due to illness or age, assistance systems can provide support in everyday life. Such systems must be able to predict precisely which movement should be performed in order to provide optimal support in real time. This prediction represents one of the greatest challenges in rehabilitation robotics and in human-robot interaction.
Dr. Isabell Wochner's research group is investigating how everyday movements can be predicted in real time using wearable sensor data and neuromechanical computer models. To this end, wearable sensors will be used in combination with AI methods to record gaze direction and body movement. The project aims to use this improved prediction to enable robotic assistance systems that support people with motor impairments in real time and thus significantly improve their quality of life.
Coming soon
+49 (0)7071-
29-88830
+49 (0)7071-
29-88830
Selected publications
Wochner, I., Schumacher, P., Martius, G., Büchler, D., Schmitt, S., Haeufle, D.F.B. (2022). Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks. CoRL 2022, https://openreview.net/forum?id=Xo3eOibXCQ8
Waldhof, M., Wochner, I., Stollenmaier, K., Parspour, N., & Schmitt, S. (2022). Design and Scaling of Exoskeleton Power Units Considering Load Cycles of Humans. Robotics, 11(5), 107. https://doi.org/10.3390/robotics11050107
Wochner, I., Nölle, L. V., Martynenko, O. V., & Schmitt, S. (2022). ‘Falling heads’: Investigating reflexive Responses to Head–neck Perturbations. Biomedical engineering online, 21(1), 1-23. https://doi.org/10.1186/s12938-022-00994-9
Martynenko, O. V., Wochner, I., Nölle, L. V., Alfaro, E. H., Schmitt, S.,.. & Vlachoutsis, S. (2021). Comparison of the Head-Neck Kinematics of Different Active Human Body Models with Experimental Data. IRCOBI Conference Proceedings, 51, 20-7. http://www.ircobi.org/wordpress/downloads/irc21/pdf-files/2121.pdf
Haeufle, D. F.B., Wochner, I., Holzmüller, D., Driess, D., Günther, M., & Schmitt, S. (2020). Muscles reduce neuronal Information load: Quantification of Control Effort in biological vs. robotic Pointing and Walking. Frontiers in Robotics and AI, 7, 77. https://doi.org/10.3389/frobt.2020.00077
Wochner, I., Driess, D., Zimmermann, H., Haeufle, D. F., Toussaint, M., & Schmitt, S. (2020). Optimality principles in human point-to-manifold reaching accounting for muscle dynamics. Frontiers in computational neuroscience, 14, 38. https://doi.org/10.3389/fncom.2020.00038
Chacon, P.F.S., Hammer, M., Wochner, I., Walter, J.R. & Schmitt, S. (2023). A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers. Computer Methods in Biomechanics and Biomedical Engineering (2023): 1-20. doi.org/10.1080/10255842.2023.2293652
Wochner, I., Nadler, T., Stollenmaier, K., Pley, C., Ilg, W., Wolfen, S., Schmitt, S., Haeufle, D.F.B. (2024) “ATARO: a muscle-driven biorobotic arm to investigate healthy and impaired motor control”, IEEE BioRob 2024, https://doi.org/10.1109/BioRob60516.2024.10719710
Sapounaki, M., Schumacher, P., Ilg, W., Giese, M., Maufroy, C., Bulling, A., Schmitt, S., Haeufle, Daniel F.B., Wochner, I., (2024): “Quantifying human upper limb stiffness responses based on a computationally efficient neuromusculoskeletal arm model”, IEEE BioRob 2024 https://doi.org/10.1109/BioRob60516.2024.10719776
Charaja, J., Wochner, I., Schumacher, P., Ilg, W., Giese, M., Maufroy, C., Bulling, A., Schmitt, S., and Haeufle, D.F.B (2024) “Generating realistic arm movements in reinforcement learning: A quantitative comparison of reward terms and task requirements,” IEEE BioRob 2024 doi.org/0.1109/BioRob60516.2024.10719719
Nölle, L. V., Wochner, I., Hammer, M., & Schmitt, S. "Using muscle-tendon load limits to assess unphysiological musculoskeletal model deformation and Hill-type muscle parameter choice." PLOS ONE (2024): 2024-04. https://doi.org/10.1371/journal.pone.0302949
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