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Wochner, Isabell

Isabell Wochner

M.Sc.

Research associate
Institute for Modelling and Simulation of Biomechanical Systems

Contact

+49 711 685 63051
Email

Nobelstr. 15
70569 Stuttgart
Deutschland
Room: 00.066

Office Hours

by appointment

Subject

Biomechanics
Motor Control Modeling
Optimality Principles
Machine Learning in Human Movement

Publications

2023
1. Wochner I, Schumacher P, Martius G, Büchler D, Schmitt S, Haeufle D. Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks. In: 6th Annual Conference on Robot Learning [Internet]. 2023. (6th Annual Conference on Robot Learning). Available from: https://openreview.net/forum?id=Xo3eOibXCQ8
  - BibTeX
  BibTeX
  @inproceedings{wochner2022learning, author = {Wochner, Isabell and Schumacher, Pierre and Martius, Georg and B{\"u}chler, Dieter and Schmitt, Syn and Haeufle, Daniel}, booktitle = {6th Annual Conference on Robot Learning}, title = {Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks}, url = {https://openreview.net/forum?id=Xo3eOibXCQ8}, year = 2023 }
2022
1. Walter JR, Wochner I, Jacob M, Stollenmaier K, Lerge P, Schmitt S. allmin: A Reduced Human All-Body Model. 2022; Available from: https://doi.org/10.18419/darus-2982
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  @article{darus-2982_2022, author = {Walter, Johannes R. and Wochner, Isabell and Jacob, Marc and Stollenmaier, Katrin and Lerge, Patrick and Schmitt, Syn}, doi = {10.18419/darus-2982}, publisher = {DaRUS}, title = {{allmin: A Reduced Human All-Body Model}}, url = {https://doi.org/10.18419/darus-2982}, version = {V1}, year = 2022 }
  DOI
  10.18419/darus-2982
2. Wochner I, Nölle LV, Martynenko OV, Schmitt S. ‘Falling heads’: investigating reflexive responses to head--neck perturbations. BioMedical Engineering OnLine [Internet]. 2022;21(1):1--23. Available from: https://link.springer.com/article/10.1186/s12938-022-00994-9
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  @article{wochner2022falling, author = {Wochner, Isabell and N{\"o}lle, Lennart V and Martynenko, Oleksandr V and Schmitt, Syn}, doi = {doi: 10.1186/s12938-022-00994-9}, journal = {BioMedical Engineering OnLine}, number = 1, pages = {1--23}, publisher = {Springer}, title = {‘Falling heads’: investigating reflexive responses to head--neck perturbations}, url = {https://link.springer.com/article/10.1186/s12938-022-00994-9}, volume = 21, year = 2022 }
  DOI
  doi: 10.1186/s12938-022-00994-9
3. Waldhof M, Wochner I, Stollenmaier K, Parspour N, Schmitt S. Design and Scaling of Exoskeleton Power Units Considering Load Cycles of Humans. Robotics [Internet]. 2022;11(5):107. Available from: https://doi.org/10.3390/robotics11050107
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  @article{Waldhof.2022, abstract = {Exoskeletons are powerful tools for aiding humans with pathological conditions, in dangerous environments or in manually exhausting tasks. Typically, they are designed for specific maximum scenarios without taking into account the diversity of tasks and the individuality of the user. To address this discrepancy, a framework was developed for personalizing an exoskeleton by scaling the components, especially the electrical machine, based on different simulated human muscle forces. The main idea was to scale a numerical arm model based on body mass and height to predict different movements representing both manual labor and daily activities. The predicted torques necessary to produce these movements were then used to generate a load/performance cycle for the power unit design. Considering these torques, main operation points of this load cycle were defined and a reference power unit was scaled and optimized. Therefore, a scalability model for an electrical machine is introduced. This individual adaptation and scaling of the power unit for different users leads to a better performance and a lighter design.}, author = {Waldhof, Marcel and Wochner, Isabell and Stollenmaier, Katrin and Parspour, Nejila and Schmitt, Syn}, doi = {10.3390/robotics11050107}, file = {robotics-11-00107:S\:\\Mitarbeiter\\01_Bibliothek\\01_Literatur\\Citavi Attachments\\robotics-11-00107.pdf:pdf}, journal = {Robotics}, number = 5, pages = 107, title = {Design and Scaling of Exoskeleton Power Units Considering Load Cycles of Humans}, url = {https://doi.org/10.3390/robotics11050107}, volume = 11, year = 2022 }
  DOI
  10.3390/robotics11050107
4. Wochner I, Schmitt S. arm26: A Human Arm Model. 2022; Available from: https://doi.org/10.18419/darus-2871
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  @article{darus-2871_2022, author = {Wochner, Isabell and Schmitt, Syn}, doi = {10.18419/darus-2871}, publisher = {DaRUS}, title = {{arm26: A Human Arm Model}}, url = {https://doi.org/10.18419/darus-2871}, version = {V1}, year = 2022 }
  DOI
  10.18419/darus-2871
2021
1. Martynenko OV, Wochner I, Nölle LV, Alfaro EH, Schmitt S, Mayer C, et al. Comparison of the Head-Neck Kinematics of Different Active Human Body Models with Experimental Data. In: Proceedings of IRCOBI Conference [Internet]. 2021. p. 105--121. (Proceedings of IRCOBI Conference). Available from: http://www.ircobi.org/wordpress/downloads/irc21/pdf-files/2121.pdf
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  @inproceedings{Martynenko., author = {Martynenko, Oleksandr V. and Wochner, Isabell and N{\"o}lle, Lennart V. and Alfaro, Eduardo H. and Schmitt, Syn and Mayer, Christian and Mishra, Atul and Ghosh, Pronoy and Chitteti, Ravi Kiran and Weber, Jens and others}, booktitle = {Proceedings of IRCOBI Conference}, pages = {105--121}, title = {Comparison of the Head-Neck Kinematics of Different Active Human Body Models with Experimental Data}, url = {http://www.ircobi.org/wordpress/downloads/irc21/pdf-files/2121.pdf}, urldate = {13.03.2023}, year = 2021 }
2020
1. Haeufle DF, Wochner I, Holzmüller D, Driess D, Günther M, Schmitt S. Muscles reduce neuronal information load: quantification of control effort in biological vs robotic pointing and walking. Frontiers in Robotics and AI [Internet]. 2020;7:77 (13pp). Available from: https://www.frontiersin.org/articles/10.3389/frobt.2020.00077/full
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  @article{haeufle2020muscles, abstract = {It is hypothesized that the nonlinear muscle characteristic of biomechanical systems simplify control in the sense that the information the nervous system has to process is reduced through off-loading computation to the morphological structure. It has been proposed to quantify the required information with an information-entropy based approach, which evaluates the minimally required information to control a desired movement, i.e., control effort. The key idea is to compare the same movement but generated by different actuators, e.g., muscles and torque actuators, and determine which of the two morphologies requires less information to generate the same movement. In this work, for the first time, we apply this measure to numerical simulations of more complex human movements: point-to-point arm movements and walking. These models consider up to 24 control signals rendering the brute force approach of the previous implementation to search for the minimally required information futile. We therefore propose a novel algorithm based on the pattern search approach specifically designed to solve this constraint optimization problem. We apply this algorithm to numerical models, which include Hill-type muscle-tendon actuation as well as ideal torque sources acting directly on the joints. The controller for the point-to-point movements was obtained by deep reinforcement learning for muscle and torque actuators. Walking was controlled by proprioceptive neural feedback in the muscular system and a PD controller in the torque model. Results show that the neuromuscular models consistently require less information to successfully generate the movement than the torque-driven counterparts. These findings were consistent for all investigated controllers in our experiments, implying that this is a system property, not a controller property. The proposed algorithm to determine the control effort is more efficient than other standard optimization techniques and provided as open source.}, author = {Haeufle, Daniel FB and Wochner, Isabell and Holzm{\"u}ller, David and Driess, Danny and G{\"u}nther, Michael and Schmitt, Syn}, doi = {https://doi.org/10.3389/frobt.2020.00077}, journal = {Frontiers in Robotics and AI}, pages = {77 (13pp)}, publisher = {Frontiers}, title = {{Muscles reduce neuronal information load: quantification of control effort in biological vs robotic pointing and walking}}, url = {https://www.frontiersin.org/articles/10.3389/frobt.2020.00077/full}, volume = 7, year = 2020 }
  DOI
  https://doi.org/10.3389/frobt.2020.00077
2. Wochner I, Driess D, Zimmermann H, Haeufle DF, Toussaint M, Schmitt S. Optimality principles in human point-to-manifold reaching accounting for muscle dynamics. Frontiers in Computational Neuroscience [Internet]. 2020;14:38. Available from: https://www.frontiersin.org/articles/10.3389/fncom.2020.00038/full
  - BibTeX
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  BibTeX
  @article{wochner2020optimality, abstract = {Human arm movements are highly stereotypical under a large variety of experimental conditions. This is striking due to the high redundancy of the human musculoskeletal system, which in principle allows many possible trajectories toward a goal. Many researchers hypothesize that through evolution, learning, and adaption, the human system has developed optimal control strategies to select between these possibilities. Various optimality principles were proposed in the literature that reproduce human-like trajectories in certain conditions. However, these studies often focus on a single cost function and use simple torque-driven models of motion generation, which are not consistent with human muscle-actuated motion. The underlying structure of our human system, with the use of muscle dynamics in interaction with the control principles, might have a significant influence on what optimality principles best model human motion. To investigate this hypothesis, we consider a point-to-manifold reaching task that leaves the target underdetermined. Given hypothesized motion objectives, the control input is generated using Bayesian optimization, which is a machine learning based method that trades-off exploitation and exploration. Using numerical simulations with Hill-type muscles, we show that a combination of optimality principles best predicts human point-to-manifold reaching when accounting for the muscle dynamics.}, author = {Wochner, Isabell and Driess, Danny and Zimmermann, Heiko and Haeufle, Daniel FB and Toussaint, Marc and Schmitt, Syn}, doi = {https://doi.org/10.3389/fncom.2020.00038}, journal = {Frontiers in Computational Neuroscience}, pages = 38, publisher = {Frontiers}, title = {Optimality principles in human point-to-manifold reaching accounting for muscle dynamics}, url = {https://www.frontiersin.org/articles/10.3389/fncom.2020.00038/full}, volume = 14, year = 2020 }
  DOI
  https://doi.org/10.3389/fncom.2020.00038
2019
1. Wochner I, Endler CA, Schmitt S, Martynenko OV. Comparison of Controller Strategies for Active Human Body Models with Different Muscle Materials. In: IRCOBI Conference Proceedings [Internet]. 2019. (IRCOBI Conference Proceedings). Available from: http://www.ircobi.org/wordpress/downloads/irc19/pdf-files/30.pdf
  - BibTeX
  BibTeX
  @inproceedings{wochner2019comparison, author = {Wochner, Isabell and Endler, Carola A. and Schmitt, Syn and Martynenko, Oleksandr V.}, booktitle = {IRCOBI Conference Proceedings}, title = {Comparison of Controller Strategies for Active Human Body Models with Different Muscle Materials}, url = {http://www.ircobi.org/wordpress/downloads/irc19/pdf-files/30.pdf}, year = 2019 }

Vita

Scientific Education

since 09/2018: PhD student at the Faculty 2 and GS SimTech, University of Stuttgart
04/2018: M.Sc. Simulation Technology, University of Stuttgart
09/2016-04/2017: exchange year, University of Toronto
10/2015: B.Sc. Simulation Technology, University of Stuttgart

Professional Positions

since 09/2018: research assistant at the CBB group, Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart
06/2018-08/2018: student research assistant at the Institute for Parallel and Distributed Systems, University of Stuttgart
10/2017-02/2018: student research assistant at the CBB group, University of Stuttgart
09/2016-04/2017: student research assistant at the Institut für Physical Therapy, University of Toronto
10/2015-08/2016: student research assistant at the CBB group, University of Stuttgart
04/2015-09/2015: bachelor thesis at the Daimler AG, Stuttgart
06/2014-03/2015: student research assistant at the CBB group, University of Stuttgart

Other positions

since 12/2018: PhD Spokesperson at the SC SimTech

Isabell Wochner

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