Abstract
This work compares the efficiency and accuracy of several muscle models, having in mind their future use in AI-based model-predictive controllers for gait simulation. The models are tested on the forward-dynamics simulation of a captured motion. Moreover, the effect of the number of modeled muscles on the efficiency is also studied. Results show that the Hill muscle model with rigid tendon is a good candidate for its use in the mentioned controllers, and that reducing the number of muscles strongly improves the computational efficiency.
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Acknowledgements
This work was funded by the Spanish MICIU under project PGC2018-095145-B-100, cofinanced by the EU through the EFRD program, and by the Galician Government under grant ED431C-2019/29.
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Mouzo, F., Michaud, F., Lamas, M., Lugris, U., Cuadrado, J. (2022). Effect of Muscle Modeling in the Efficiency and Accuracy of the Forward-Dynamics Simulation of Human Gait. In: Torricelli, D., Akay, M., Pons, J.L. (eds) Converging Clinical and Engineering Research on Neurorehabilitation IV. ICNR 2020. Biosystems & Biorobotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-030-70316-5_48
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DOI: https://doi.org/10.1007/978-3-030-70316-5_48
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