The why and how of human gait simulations (HGS)2.5 ECTS
(englische Bezeichnung: The why and how of human gait simulations)
(Prüfungsordnungsmodul: The why and how of human gait simulations)

Lehrveranstaltungen:

• • The why and how of human gait simulations
    (Seminar, 2 SWS, Anne Koelewijn, Di, 10:15 - 11:45, 00.010)

Lernziele und Kompetenzen:

Learning objectives:

• Set up a trajectory optimization problem to solve for a gait simulation

• Be familiar with different approaches to solving gait simulations

• Be able to select an approach to solve a specific simulation problem

• Know the state-of-the-art gait simulation methods used at FAU and universities in Germany and abroad

Literatur:

• Kelly, Matthew. "An introduction to trajectory optimization: How to do your own direct collocation." SIAM Review 59.4 (2017): 849-904.

• Anderson, Frank C., and Marcus G. Pandy. “Dynamic Optimization of Human Walking.” Journal of Biomechanical Engineering 123, no. 5 (May 16, 2001): 381–90. https://doi.org/10.1115/1.1392310.

• Van den Bogert, Antonie J., et al. "Predictive musculoskeletal simulation using optimal control: effects of added limb mass on energy cost and kinematics of walking and running." Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology 226.2 (2012): 123-133.

• Dorschky, Eva, Daniel Krüger, Nicolai Kurfess, Heiko Schlarb, Sandro Wartzack, Bjoern M. Eskofier, and Antonie J. van den Bogert. “Optimal Control Simulation Predicts Effects of Midsole Materials on Energy Cost of Running.” Computer Methods in Biomechanics and Biomedical Engineering 0, no. 0 (April 16, 2019): 1–11. https://doi.org/10.1080/10255842.2019.1601179.

• Dzeladini, Florin, Jesse van den Kieboom, and Auke Ijspeert. “The Contribution of a Central Pattern Generator in a Reflex-Based Neuromuscular Model.” Frontiers in Human Neuroscience 8 (2014). https://doi.org/10.3389/fnhum.2014.00371.

• Handford, M. L., and M. Srinivasan. “Energy-Optimal Human Walking With Feedback-Controlled Robotic Prostheses: A Computational Study.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 26, no. 9 (September 2018): 1773–82. https://doi.org/10.1109/TNSRE.2018.2858204.

• Hiley, Michael J., and Maurice R. Yeadon. “Investigating Optimal Technique in a Noisy Environment: Application to the Upstart on Uneven Bars.” Human Movement Science 32, no. 1 (February 2013): 181–91. https://doi.org/10.1016/j.humov.2012.11.004.

• Koelewijn, Anne D., Eva Dorschky, and Antonie J. van den Bogert. “A Metabolic Energy Expenditure Model with a Continuous First Derivative and Its Application to Predictive Simulations of Gait.” Computer Methods in Biomechanics and Biomedical Engineering 21, no. 8 (June 11, 2018): 521–31. https://doi.org/10.1080/10255842.2018.1490954.

• Lin, Yi-Chung, and Marcus G. Pandy. “THREE-DIMENSIONAL DATA-TRACKING DYNAMIC OPTIMIZATION SIMULATIONS OF HUMAN LOCOMOTION GENERATED BY DIRECT COLLOCATION.” Journal of Biomechanics. Accessed May 30, 2017. https://doi.org/10.1016/j.jbiomech.2017.04.038.

• Miller, Ross H. “A Comparison of Muscle Energy Models for Simulating Human Walking in Three Dimensions.” Journal of Biomechanics 47, no. 6 (April 11, 2014): 1373–81. https://doi.org/10.1016/j.jbiomech.2014.01.049.

• Miller, Ross H., Aryeh Y. Esterson, and Jae Kun Shim. “Joint Contact Forces When Minimizing the External Knee Adduction Moment by Gait Modification: A Computer Simulation Study.” The Knee 22, no. 6 (December 1, 2015): 481–89. https://doi.org/10.1016/j.knee.2015.06.014.

• Mombaur, Katja, and Debora Clever. “Inverse Optimal Control as a Tool to Understand Human Movement.” In Geometric and Numerical Foundations of Movements, edited by Jean-Paul Laumond, Nicolas Mansard, and Jean-Bernard Lasserre, 163–86. Springer Tracts in Advanced Robotics 117. Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-51547-2_8.
  

Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:

1. Artificial Intelligence (Master of Science)
   (Po-Vers. 2021s | TechFak | Artificial Intelligence (Master of Science) | Gesamtkonto | Nebenfach | Nebenfach Artificial Intelligence in Biomedical Engineering | The why and how of human gait simulations)

Studien-/Prüfungsleistungen:

The why and how of human gait simulations (Prüfungsnummer: 68391)

(englischer Titel: The why and how of human gait simulations)

Prüfungsleistung, Seminarleistung, benotet, 2.5 ECTS

Anteil an der Berechnung der Modulnote: 100.0 %

weitere Erläuterungen:
presentation

Erstablegung: WS 2022/2023, 1. Wdh.: SS 2023

1. Prüfer:

Anne Koelewijn