NM Track

The Neuromechanics Track is to award the team that conducts and reports the most novel biomechanical study using our simulation environment. To win this track, one should:

Our review board led by Seungmoon Song (smsong@stanford.edu) will select the paper that presents the most novel study in any physiological aspects of human locomotion using our simulation environment. The topics of interest include but are not limited to:

  • motor control (e.g. biologically plausible motor control models [Dzeladini et al., 2014; Song and Geyer, 2015; Aoi et al., 2019]
  • optimality principle (e.g. inverse optimization [Mombaur et al., 2010]; objective functions for predicting behaviors [Ackermann and Van den Bogert, 2010; Falisse et al., 2019]
  • musculoskeletal biomechanics (e.g. investigating effects of muscle components [Miller et al., 2010])
  • assistive devices (e.g. designing prosthetic legs [Handford and Srinivasan, 2018] or exoskeletons [Dembia et al., 2017]
  • gait pathology (e.g. gait changes due to muscle deficit [Ong et al., 2019] or age [Song and Geyer, 2018])

The prizes for the winner are:

FAQ (will be updated)

  1. What are the criteria in reviewing the papers? In addition to the final score you received in the competition, we will be looking for similar things as in regular research papers: significance, relevance, originality, scientific soundness, and quality of presentation.
  2. Can we change the human model or simulation environment? You can do whatever you find necessary for your study and paper. For example, investigating how changes in muscle properties affect walking behavior will be a perfectly valid study.


  • Ackermann, M., & Van den Bogert, A. J. (2010). Optimality principles for model-based prediction of human gait. Journal of biomechanics, 43(6), 1055-1060.
  • Aoi, S., Ohashi, T., Bamba, R., Fujiki, S., Tamura, D., Funato, T., … & Tsuchiya, K. (2019). Neuromusculoskeletal model that walks and runs across a speed range with a few motor control parameter changes based on the muscle synergy hypothesis. Scientific reports, 9(1), 369.
  • Dembia, C. L., Silder, A., Uchida, T. K., Hicks, J. L., & Delp, S. L. (2017). Simulating ideal assistive devices to reduce the metabolic cost of walking with heavy loads. PloS one, 12(7), e0180320.
  • Dzeladini, F., Van Den Kieboom, J., & Ijspeert, A. (2014). The contribution of a central pattern generator in a reflex-based neuromuscular model. Frontiers in human neuroscience, 8, 371.
  • Falisse A., Serrancolí, G., Dembia, C. L., Gillis, J., Jonkers, I., & De Groote, F. (2019). Rapid predictive simulations with complex musculoskeletal models suggest that diverse healthy and pathological human gaits can emerge from similar control strategies. Journal of the Royal Society Interface, to appear.
  • Handford, M. L., & Srinivasan, M. (2018). Energy-optimal human walking with feedback-controlled robotic prostheses: A computational study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(9), 1773-1782.
  • Miller, R. H., Umberger, B. R., & Caldwell, G. E. (2012). Limitations to maximum sprinting speed imposed by muscle mechanical properties. Journal of biomechanics, 45(6), 1092-1097.
  • Mombaur, K., Truong, A., & Laumond, J. P. (2010). From human to humanoid locomotion—an inverse optimal control approach. Autonomous robots, 28(3), 369-383.
  • Ong, C. F., Geijtenbeek, T., Hicks, J. L., & Delp, S. L. (2019). Predicting gait adaptations due to ankle plantarflexor muscle weakness and contracture using physics-based musculoskeletal simulations. BioRxiv, 597294.
  • Song, S., & Geyer, H. (2015). A neural circuitry that emphasizes spinal feedback generates diverse behaviours of human locomotion. The Journal of physiology, 593(16), 3493-3511.
  • Song, S., & Geyer, H. (2018). Predictive neuromechanical simulations indicate why walking performance declines with ageing. The Journal of physiology, 596(7), 1199-1210.