Persistent Identifier
|
doi:10.18419/darus-2871 |
Publication Date
|
2022-06-07 |
Title
| arm26: A Human Arm Model |
Author
| Wochner, Isabell (University of Stuttgart) - ORCID: 0000-0002-2820-5791
Schmitt, Syn (University of Stuttgart) - ORCID: 0000-0002-7768-8961 |
Point of Contact
|
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Wochner, Isabell (University of Stuttgart)
Schmitt, Syn (University of Stuttgart) |
Description
| An arm model parametrised using generic literature data for the geometry of the skeleton including attachment points for ligaments and muscles. This arm26 model consists of a musculoskeletal model of the arm with two degrees of freedom actuated by six muscles. The model is prepared to run muscle-driven simulation using a simple biological motor control model. The file contains an archive including all relevant data to run the simulation in the simulator demoa. This needs to be installed separately and is available as open source too (get-demoa.com). If you use this model, please cite the related publications together with this dataset. |
Subject
| Engineering; Mathematical Sciences; Medicine, Health and Life Sciences; Physics |
Keyword
| Biomechanics https://www.wikidata.org/wiki/Q193378 (Wikidata)
Arm Model
Biological Motor Control |
Related Publication
| 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 doi: 10.3389/fncom.2020.00038 https://doi.org/10.3389/fncom.2020.00038
Stollenmaier, K., Ilg, W., & Haeufle, D. F. (2020). Predicting perturbed human arm movements in a neuro-musculoskeletal model to investigate the muscular force response. Frontiers in bioengineering and biotechnology, 8, 308 doi: 10.3389/fbioe.2020.00308 https://doi.org/10.3389/fbioe.2020.00308
Driess, D., Zimmermann, H., Wolfen, S., Suissa, D., Haeufle, D., Hennes, D., Toussaint, M. and 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 https://doi.org/10.1109/ICRA.2018.8463160 |
Contributor
| Researcher : Dan Rouven Suissa
Researcher : Katrin Stollenmaier |
Funding Information
| DFG: EXC 2075 - 390740016 |
Depositor
| Wochner, Isabell |
Deposit Date
| 2022-05-19 |
Related Dataset
| Schmitt, Syn, 2022, "demoa-base: A Biophysics Simulator for Muscle-driven Motion", https://doi.org/10.18419/darus-2550, DaRUS |