OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement. Dynamic simulations of movement allow one to study neuromuscular coordination, analyze athletic performance, and estimate internal loading of the musculoskeletal system. Simulations can also be used to identify the sources of pathological movement and establish a scientific basis for treatment planning. We have developed a freely available, open-source software system (OpenSim) that lets users develop models of musculoskeletal structures and create dynamic simulations of a wide variety of movements. We are using this system to simulate the dynamics of individuals with pathological gait and to explore the biomechanical effects of treatments. OpenSim provides a platform on which the biomechanics community can build a library of simulations that can be exchanged, tested, analyzed, and improved through a multi-institutional collaboration. Developing software that enables a concerted effort from many investigators poses technical and sociological challenges. Meeting those challenges will accelerate the discovery of principles that govern movement control and improve treatments for individuals with movement pathologies.

References in zbMATH (referenced in 17 articles )

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  1. Wang, Aihui; Lu, Junlan; Ge, Yifei; Yu, Jun; Zhang, Shuaishuai: Simulation of limb rehabilitation robot based on OpenSim (2020)
  2. Ezati, Mahdokht; Ghannadi, Borna; McPhee, John: A review of simulation methods for human movement dynamics with emphasis on gait (2019)
  3. Muller, A., Pontonnier, C., Puchaud, P., Dumont, G.: CusToM: a Matlab toolbox for musculoskeletal simulation (2019) not zbMATH
  4. Jason K. Moore; Antonie van den Bogert: opty: Software for trajectory optimization and parameter identification using direct collocation (2018) not zbMATH
  5. Manish Sreenivasa; Monika Harant: ModelFactory: A Matlab/Octave based toolbox to create human body models (2018) arXiv
  6. Menegaldo, Luciano L.: Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters (2017)
  7. Muller, Antoine; Pontonnier, Charles; Dumont, Georges: Uncertainty propagation in multibody human model dynamics (2017)
  8. Ehsani, Hossein; Rostami, Mostafa; Parnianpour, Mohammad: A closed-form formula for the moment arm matrix of a general musculoskeletal model with considering joint constraint and motion rhythm (2016)
  9. Ovesy, Marzieh; Nazari, Mohammad Ali; Mahdavian, Mohammad: Equivalent linear damping characterization in linear and nonlinear force-stiffness muscle models (2016)
  10. Gamus, Benny; Or, Yizhar: Dynamic bipedal walking under stick-slip transitions (2015)
  11. Sachdeva, Prashant; Sueda, Shinjiro; Bradley, Susanne; Fain, Mikhail; Pai, Dinesh K.: Biomechanical simulation and control of hands and tendinous systems (2015)
  12. Hatz, Kathrin: Efficient numerical methods for hierarchical dynamic optimization with application to cerebral palsy gait modeling (2014)
  13. Nha, Kyung Wook; Dorj, Ariunzaya; Feng, Jun; Shin, Jun Ho; Kim, Jong In; Kwon, Jae Ho; Kim, Kyungsoo; Kim, Yoon Hyuk: Application of computational lower extremity model to investigate different muscle activities and joint force patterns in knee osteoarthritis patients during walking (2013)
  14. Lin, Yi-Chung; Kim, Hyung Joo; Pandy, Marcus G.: A computationally efficient method for assessing muscle function during human locomotion (2011)
  15. Stavness, Ian; Lloyd, John E.; Payan, Yohan; Fels, Sidney: Coupled hard-soft tissue simulation with contact and constraints applied to jaw-tongue-hyoid dynamics (2011)
  16. Seth, Ajay; Sherman, Michael; Eastman, Peter; Delp, Scott: Minimal formulation of joint motion for biomechanisms (2010)
  17. Macleod, R. S.; Stinstra, J. G.; Lew, S.; Whitaker, R. T.; Swenson, D. J.; Cole, M. J.; Krüger, J.; Brooks, D. H.; Johnson, C. R.: Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples (2009)