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FG Häufle

Modellierung der Bewegungskontrolle

Die Forschungsgruppe „Multi-Level Modeling in Motor Control and Rehabilitation Robotics“ untersucht die Erzeugung und Kontrolle aktiver biologischer Bewegungen. Wir entwickeln Modelle und Computersimulationen des neuro-muskulo-skelettalen Systems. In einem Mehrskalen-Ansatz können wir die unterschiedlichen hierarchischen Ebenen berücksichtigen, die zur Bewegungserzeugung beitragen. Unser interdisziplinärer Ansatz integriert Konzepte der Biophysik, Biomechanik und Motorik.

Mit diesem Ansatz leisten wir einen Beitrag zur Aufklärung der grundlegenden sensomotorischen Mechanismen in der Kontrolle von Bewegungen. Außerdem untersuchen wir die Zusammenhänge und Auswirkungen bei deren Dysfunktion durch neurologische Erkrankungen. Mit einem tieferen Verständnis der kausalen Zusammenhänge der Bewegungsdynamik, der beeinträchtigten Kontrolle und der neuro-muskulären Interaktion legen wir die Grundlagen für funktionale Assistenzsysteme im Bereich Rehabilitationsrobotik.

Die Gruppe ist Teil der Regionalen Forschungsallianz „System Mensch“ zwischen der Universität Tübingen und der Universität Stuttgart. Unser Ziel ist es die neuro-wissenschaftliche Expertise in Tübingen mit der Expertise in Systemwissenschaft und Computer Simulation am Stuttgarter Exzellenzcluster SimTech (SC SimTech) zusammenzuführen.


Model-based concepts for the control of assistive devices

We develop neuro-musculo-skeletal computer simulations of impaired motor control. These models can be used to estimate desing criteria for assistive devices. Furthermore, such models could be used to evaluate control strategies for assistive devices already in the design phase.

In collaboration with Syn Schmitt at Uni Stuttgart (https://www.imsb.uni-stuttgart.de/research/cbb/) and Christoph Keplinger at MPI-IS (https://rm.is.mpg.de/)

Selected publications: Stollenmaier, K., Rist, I. S., Izzi, F., & Haeufle, D. F. B. (2020). Simulating the response of a neuro-musculoskeletal model to assistive forces: implications for the design of wearables compensating for motor control deficits. *2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)*, 779–784. https://doi.org/10.1109/BioRob49111.2020.9224411

Compensating for expected step-down perturbations: modelling anticipatory control strategies in human walking

In preparation for a step-down, humans change the activation patterns of individual muscles already during the last ground contact before the step-down. By reducing muscle activity in the stance leg, they lower the height of their body's center of mass (Müller et al., 2020). In this project, we further investigate this anticipatory adaptation strategy with the help of a muscle reflex model and perturbation simulations.

Contributors: Lucas Schreff, in collaboration with Roy Müller at Klinikum Bayreuth (<https://www.researchgate.net/lab/GaitLab-Klinikum-Bayreuth-Roy-Mueller>)

Selected publications: Müller, R., Vielemeyer, J. & Häufle, D. F. B. (2020). Negotiating ground level perturbations in walking: Visual perception and expectation of curb height modulate muscle activity. Journal of Biomechanics, 113, 110121. doi.org/10.1016/j.jbiomech.2020.110121

Modelling motor control in rare neurodegenerative diseases

Motor control abnormalities are key symptoms in several rare neurodegenerative diseases. We contribute to a project in which our colleagues study the development and progression of movement dysfunctions by assessing kinematics of premanifest and manifest patients. Our role in this project is to develop neuro-mechanical models with the aim to develop a more detailed understanding of the progressive character of motor control dysfunction. Especially, we are interested in the relationship between altered neuromuscular reflexes and kinematics.

Contributors: Christian Laßmann, in Collaboration with Winfried Ilg and Ludger Schoels, HIH Tübingen

The contribution of muscles to the control of movement: quantifying morphological computation

Muscle fibres possess complex, nonlinear viscous properties, which allow to counter unexpected perturbations with zero time delay. This feature is critical during agile, legged locomotion, e.g., hopping and running, where sensory blindness due to neurotransmission delays can result in dangerous falls. By expanding the current understanding of the stabilising capacity of fibre viscosity, we aim to unveil new design principles for technical solutions in the field of legged robotics and medical rehabilitation/assistance.

Contributors: Fabio Izzi and Matthew Araz, In collaboration with MPI-IS (Link: dlg.is.mpg.de) and Uni Stuttgart (Link: www.inspo.uni-stuttgart.de/institut/av/)

Mo, A., Izzi, F., Haeufle, D. F. B., & Badri-Spröwitz, A. (2020). Effective Viscous Damping Enables Morphological Computation in Legged Locomotion. *Frontiers in Robotics and AI - Soft Robotics*, *7*(August), 1–15. doi.org/10.3389/frobt.2020.00110

Leveraging morphological computation in autonomous control

Muscle-driven biological systems effortlessly outperform even the most sophisticated robots in terms of adaptivity, longevity and complexity of behaviour. However, widespread adoption of biomimetic devices is partially hindered by the difficulty of precisely controlling muscular morphologies, for which classical control approaches tend to fail. We therefore aim to control these systems with novel reinforcement learning techniques, leveraging their ability to deal with complex nonlinear actuators. The combination of autonomously learning agents with biomechanically inspired morphologies may unlock some of the secrets encoded by millions of years of evolution, while dealing with the unique control difficulties that these morphologies exhibit.

Contributors: Pierre Schumacher, in Collaboration with Georg Martius at MPI-IS (<https://al.is.mpg.de/>) and Dieter Büchler at MPI-IS (https://www.is.mpg.de/person/dbuechler) and Syn Schmitt at Uni Stuttgart (Link: www.imsb.uni-stuttgart.de/research/cbb/).

Selected publications: 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*, Research topic: Recent Trends in Morphological Com. doi.org/10.3389/frobt.2020.00077

 Matthew Araz
Matthew Araz
Motor Control Modeling

Dr. Daniel Haeufle
Dr. Daniel HaeufleResearch Group Leader
Motor Control Modeling


 Colin Halupczok
Colin HalupczokMaster Student
Motor Control Modeling

+49 (0)7071-

 Malte Hendrickson
Malte Hendrickson
Motor Control Modeling

 Junya Inoue
Junya InouePhD Student
Motor Control Modeling

 Fabio Izzi
Fabio IzziPhD Student
Motor Control Modeling


 Jan Kerner
Jan KernerBachelor Student
Motor Control Modeling

 Lorenz Krause
Lorenz KrauseMaster Student
Motor Control Modeling

 Christian Lassmann
Christian LassmannBachelor Student
Motor Control Modeling

 Lucas Schreff
Lucas SchreffGuest Researcher
Motor Control Modeling

 Pierre Schumacher
Pierre SchumacherPhD Student
Motor Control Modeling

+49 (7071)

 Johanna Sellhorn-Timm
Johanna Sellhorn-Timm
Motor Control Modeling

 Valerie Wendt
Valerie WendtMaster Student
Motor Control Modeling

+49 (0)7071-



  1. Günther, M., Rockenfeller, R., Weihmann, T., Haeufle, D. F. B., Götz, T., & Schmitt, S. (2021). Rules of nature’s Formula Run: Muscle mechanics during late stance is the key to explaining maximum running speed. Journal of Theoretical Biology, 523, 110714. doi.org/10.1016/j.jtbi.2021.110714
  2. Karakostis, F. A., Haeufle, D., Anastopoulou, I., Moraitis, K., Hotz, G., Tourloukis, V., & Harvati, K. (2021). Biomechanics of the human thumb and the evolution of dexterity. Current Biology, 31(6), 1317-1325.e8. doi.org/10.1016/j.cub.2020.12.041
  3. Walter, J. R., Günther, M., Haeufle, D. F. B., & Schmitt, S. (2021). A geometry- and muscle-based control architecture for synthesising biological movement. Biological Cybernetics, 115(1), 7–37. doi.org/10.1007/s00422-020-00856-4
  4. Haeufle, D., Stollenmaier, K., Heinrich, I., Schmitt, S., Ghazi-Zahedi, K. (2020). Morphological computation increases from lower- to higher-level of biological motor control hierarchy, Frontiers in Robotics and AI. doi.org/10.3389/frobt.2020.511265.
  5. 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, Research topic: Recent Trends in Morphological Com. doi.org/10.3389/frobt.2020.00077
  6. Haeufle, D. F. B., Siegel, J., Hochstein, S., Schmitt, S., Siebert, T., Gusew, A., … Stutzig, N. (2020). Energy Expenditure of Dynamic Submaximal Human Plantarflexion Movements: Model Prediction and Validation by in-vivo Magnetic Resonance Spectroscopy. Frontiers in Bioengineering and Biotechnology, 8(June), 1–18. doi.org/10.3389/fbioe.2020.00622
  7. Stollenmaier, K., Ilg, W., & Haeufle, D. F. B. (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.org/10.3389/fbioe.2020.00308
  8. Glenday, J. D., Steinhilber, B., Jung, F., & Haeufle, D. F. B. (2020). Development of a subject-specific musculoskeletal model of the wrist to predict frictional work dissipated due to tendon gliding resistance in the carpal tunnel. Computer Methods in Biomechanics and Biomedical Engineering, 0(0), 1–14. doi.org/10.1080/10255842.2020.1862094
  9. Müller, R., Vielemeyer, J., & Haeufle, D. F. B. (2020). Negotiating ground level perturbations in walking: anticipatory and predictive control strategies modulate muscle activity based on visual perception and expectation of curb height. Journal of Biomechanics. accepted on October 22nd, 2020.
  10. Wochner, I., Driess, D., Zimmermann, H., Haeufle, D. F. B., Toussaint, M., & Schmitt, S. (2020). Optimality Principles in Human Point-to-Manifold Reaching Accounting for Muscle Dynamics. Frontiers in Computational Neuroscience, 14. doi.org/10.3389/fncom.2020.00038
  11. Rockenfeller, R., Günther, M., Stutzig, N., Haeufle, D. F. B., Siebert, T., Schmitt, S., … Götz, T. (2020). Exhaustion of Skeletal Muscle Fibers Within Seconds: Incorporating Phosphate Kinetics Into a Hill-Type Model. Frontiers in Physiology, 11. doi.org/10.3389/fphys.2020.00306
  12. Mo, A., Izzi, F., Haeufle, D. F. B., & Badri-Spröwitz, A. (2020). Effective Viscous Damping Enables Morphological Computation in Legged Locomotion. Frontiers in Robotics and AI, Soft Robotics. doi.org/10.3389/frobt.2020.00110
  13. Schmitt, S., Günther, M., & Häufle, D. F. B. (2019). The dynamics of the skeletal muscle: A systems biophysics perspective on muscle modeling with the focus on Hill‐type muscle models. GAMM-Mitteilungen, e201900013. doi.org/10.1002/gamm.201900013
  14. Hammer, M., Günther, M., Haeufle, D. F. B., & Schmitt, S. (2019). Tailoring anatomical muscle paths: a sheath-like solution for muscle routing in musculoskeletal computer models. Mathematical Biosciences, 311(March), 68–81. doi.org/10.1016/j.mbs.2019.02.004
  15. Haeufle, D. F. B., Schmortte, B., Geyer, H., Müller, R., & Schmitt, S. (2018). The Benefit of Combining Neuronal Feedback and Feed-Forward Control for Robustness in Step Down Perturbations of Simulated Human Walking Depends on the Muscle Function. Frontiers in Computational Neuroscience, 12(80). doi.org/10.3389/fncom.2018.00080
  16. Günther, M., Haeufle, D. F. B., & Schmitt, S. (2018). The basic mechanical structure of the skeletal muscle machinery: One model for linking microscopic and macroscopic scales. Journal of Theoretical Biology, 456, 137–167. doi.org/10.1016/j.jtbi.2018.07.023
  17. Bayer, A., Schmitt, S., Günther, M., & Haeufle, D. F. B. (2017). The influence of biophysical muscle properties on simulating fast human arm movements. Computer Methods in Biomechanics and Biomedical Engineering 20(8):803-821. doi.org/10.1080/10255842.2017.1293663
  18. Brown, N., Bubeck, D., Haeufle, D., Alt, W., & Schmitt, S. (2017). Weekly time course of adaptation to intensive strength training – a case study. Frontiers in Physiology.
  19. Kleinbach, C., Martynenko, O., Promies, J., Haeufle, D. F. B., Fehr, J., & Schmitt, S. (2017). Implementation and validation of the extended Hill-type muscle model with robust routing capabilities in LS-DYNA for active human body models. BioMedical Engineering OnLine, 16(1), 109. doi.org/10.1186/s12938-017-0399-7
  20. Ghazi-Zahedi, K., Haeufle, D. F. B., Montúfar, G., Schmitt, S., Ay, N. (2016). Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping. Frontiers in Robotics and AI, 3(July). doi.org/10.3389/frobt.2016.00042
  21. Haeufle, D., Bäuerle, T., Steiner J., Bremicker, L., Schmitt, S., Bechinger, C. (2016). External control strategies for self-propelled particles: optimizing navigational efficiency in the presence of limited resources, Physical Review E, 94(1), 1–8. doi.org/10.1103/PhysRevE.94.012617
  22. Mörl, F., Siebert, T., & Häufle, D. (2015). Contraction dynamics and function of the muscle-tendon complex depend on the muscle fibre-tendon length ratio: a simulation study. Biomechanics and Modeling in Mechanobiology, 15(1), 245–258. doi.org/10.1007/s10237-015-0688-7
  23. Müller, R., Haeufle, D. F. B., & Blickhan, R. (2015). Preparing the leg for ground contact in running: the contribution of feed-forward and visual feedback. Journal of Experimental Biology, 218, 451–457. doi.org/10.1242/jeb.113688
  24. Haeufle, D.F.B., M Günther, A Bayer, and S Schmitt (2014). Hill-Type Muscle Model with Serial Damping and Eccentric Force-Velocity Relation. Journal of Biomechanics 47 (6), 1531–1536. doi.org/10.1016/j.jbiomech.2014.02.009
  25. Haeufle, D.F.B., Michael Günther, Günther Wunner, and Syn Schmitt (2014): Quantifying Control Effort of Biological and Technical Movements: An Information-Entropy-Based Approach. Physical Review E 89 (1), 012716. doi.org/10.1103/PhysRevE.89.012716
  26. Schmitt, S., Günther, M., Bayer, A., Rupp, T., Häufle, D.F.B. (2013). Theoretical Hill-Type Muscle and Stability: Numerical Model and Application. Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 570878. doi.org/10.1155/2013/570878
  27. Seyfarth, A., Grimmer, S., Haeufle, D.F.B., and Kalveram, K-T. (2012). Can Robots Help to Understand Human Locomotion? AT - Automatisierungstechnik 60(11), 653–661.
  28. Haeufle, D.F.B., Worobets, J., Wright, I., Haeufle, J., Stefanyshyn, D. (2012). Golfers do not respond to changes in shaft mass properties in a mechanically predictable way. Sports Engineering 15(4), 215-220. doi.org/10.1007/s12283-012-0104-9
  29. Günther, M., Röhrle, O., Häufle, D.F.B., Schmitt, S. (2012). Spreading out muscle mass within a Hill-type model: a computer simulation study. Computational and Mathematical Methods in Medicine, 2012, 848630. doi.org/10.1155/2012/848630
  30. Schmitt, S., Häufle, D.F.B., Blickhan, R., Günther, M. (2012). Nature as an engineer: one simple concept of a bio-inspired functional artificial muscle. Bioinspiration & Biomimetics, 7 (2012) 036022. doi.org/10.1088/1748-3182/7/3/036022
  31. Haeufle, D.F.B., Günther, M., Blickhan, R., Schmitt, S. (2012). Can Quick Release Experiments Reveal the Muscle Structure? A Bionic Approach. Journal of Bionic Engineering, 9(2), 211-223. doi.org/10.1016/S1672-6529(11)60115-7
  32. Haeufle, D.F.B., Günther, M., Blickhan, R., Schmitt, S. (2012). Proof-of-concept: model based bionic muscle with hyperbolic force-velocity relation. Applied Bionics and Biomechanics, 9(3). doi.org/10.3233/ABB-2011-0052
  33. Haeufle, D.F.B., Grimmer, S., Kalveram, K.T., Seyfarth, A. (2012). Integration of intrinsic muscle properties, feed-forward and feedback signals for generating and stabilizing hopping. Journal of the Royal Society, Interface 9(72), 1458-69. doi.org/10.1098/rsif.2011.0694
  34. Kalveram, K.T., Haeufle, D.F.B., Seyfarth, A., Grimmer, S. (2012). Energy management that generates terrain following versus apex-preserving hopping in man and machine. Biological Cybernetics, 106(1), 1-13. doi.org/10.1007/s00422-012-0476-8
  35. Haeufle, D.F.B., Grimmer, S., & Seyfarth, A. (2010). The role of intrinsic muscle properties for stable hopping - stability is achieved by the force - velocity relation. Bioinspiration & Biomimetics, 5(1), 016004 (11pp). doi.org/10.1088/1748-3182/5/1/016004
Leitung Foschungsgruppe
PD Dr. Daniel HäufleTelefon 07071 29-88873daniel.haeufle@uni-tuebingen.deAnschrift

Zentrum für Neurologie
Hertie-Institut für klinische Hirnforschung

Otfried-Müller-Str. 25
72076 Tübingen

Tel.: +49 (0)7071 29-88873
Fax: +49 (0)7071 29-25011