Dynamic Simulation of the Hand

  • Shinjiro Sueda
  • Dinesh K. PaiEmail author
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 95)


Robots and simulations provide complementary approaches for exploring different aspects of hand modeling. Robotic systems have the advantage in that all physical interactions, such as contact between tendons and bones, are automatically and correctly taken into account. Conversely, software simulations can more easily incorporate different material models, muscle mechanics, or even pathologies. Previously, no software for dynamic simulation could efficiently handle the complex routing and contact constraints of the hand. We address these challenges with a new simulation framework well suited for modeling the hand. We use the spline basis as the system’s dynamic degrees of freedom, and place them where they are most needed, such as at the pulleys of the fingers. Previous biomechanical simulation approaches, based on either line-of-force or solid mechanics models, are not well-suited for the hand, due to the complex routing of tendons around various biomechanical constraints such as sheaths and pulleys. In line-of-force models, wrapping surfaces are used to approximate the curved paths of tendons and muscles near and around joints, but these surfaces affect only the kinematics, and not the dynamics, of musculotendons. In solid mechanics models, the fiber-like properties of muscles are not directly represented and must be added on as auxiliary functions. Moreover, contact constraints between bones, tendons, and muscles must be detected and resolved with a general purpose collision scheme. Neither of these approaches efficiently handles both the dynamics of the musculotendons and the complex routing constraints, while our approach resolves these issues.


Biomechanics Computer simulation Dynamics Constraints Finger Hand 


  1. 1.
    S.L. Delp, F.C. Anderson, A.S. Arnold, P. Loan, A. Habib, C.T. John, E. Guendelman, D.G.Thelen, OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54(11), 1940–1950 (2007). Google Scholar
  2. 2.
    M. Damsgaard, J. Rasmussen, S. Christensen, E. Surma, M. Dezee, Analysis of musculoskeletal systems in the AnyBody Modeling System. Simul. Model. Pract. Theory 14(8), 1100–1111 (2006)CrossRefGoogle Scholar
  3. 3.
    H. Lipson, A relaxation method for simulating the kinematics of compound nonlinear mechanisms. ASME J. Mech. Des. 128, 719–728 (2006)CrossRefGoogle Scholar
  4. 4.
    E. Johnson, K. Morris, T. Murphey, A variational approach to strand-based modeling of the human hand, in Algorithmic Foundation of Robotics VIII, ed. by G. Chirikjian, H. Choset, M. Morales, T. Murphey. Series Springer Tracts in Advanced Robotics, vol. 57 (Springer, Berlin, 2009), pp. 151–166Google Scholar
  5. 5.
    W. Tsang, K. Singh, F. Eugene, Helping hand: an anatomically accurate inverse dynamics solution for unconstrained hand motion, in Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (2005), pp. 319–328Google Scholar
  6. 6.
    D.K. Pai, Muscle mass in musculoskeletal models. J. Biomech. 43(11), 2093–2098 (2010)CrossRefMathSciNetGoogle Scholar
  7. 7.
    S.W. Lee, D.G. Kamper, Modeling of multiarticular muscles: importance of inclusion of tendon-pulley interactions in the finger. IEEE Trans. Biomed. Eng. 56(9), 2253–2262 (2009)CrossRefGoogle Scholar
  8. 8.
    A. Deshpande, R. Balasubramanian, J. Ko, Y. Matsuoka, Acquiring variable moment arms for index finger using a robotic testbed. IEEE Trans. Biomed. Eng. 57(8), 2034–2044 (2010)CrossRefGoogle Scholar
  9. 9.
    J. Teran, S. Blemker, V.N.T. Hing, R. Fedkiw, Finite volume methods for the simulation of skeletal muscle, in Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (2003), pp. 68–74Google Scholar
  10. 10.
    S.S. Blemker, S.L. Delp, Three-dimensional representation of complex muscle architectures and geometries. Ann. Biomed. Eng. 33(5), 661–673 (2005)CrossRefGoogle Scholar
  11. 11.
    K.R. Kaufman, D.A. Morrow, G.M. Odegard, T.L.H. Donahue, P.J. Cottler, S. Ward, R. Lieber, 3d model of skeletal muscle to predict intramuscular pressure, in American Society of Biomechanics Annual Conference (2010)Google Scholar
  12. 12.
    D.I.W. Levin, J. Litven, G.L. Jones, S. Sueda, D.K. Pai, Eulerian solid simulation with contact. ACM Trans. Graph. 30(4), 36:1–36:9 (2011)Google Scholar
  13. 13.
    J. Spillmann, M. Teschner, An adaptive contact model for the robust simulation of knots. Comput. Graph. Forum 27(2), 497–506 (2008)CrossRefGoogle Scholar
  14. 14.
    M. Bergou, B. Audoly, E. Vouga, M. Wardetzky, E. Grinspun, Discrete viscous threads. ACM Trans. Graph. 29(4), 116:1–116:10 (2010)Google Scholar
  15. 15.
    D.K. Pai, Strands: interactive simulation of thin solids using cosserat models. Comput. Graph. Forum 21(3), 347–352 (2002)CrossRefGoogle Scholar
  16. 16.
    A. Theetten, L. Grisoni, C. Andriot, B. Barsky, Geometrically exact dynamic splines. Comput. Aided Des. 40(1), 35–48 (2008)CrossRefGoogle Scholar
  17. 17.
    S. Sueda, A. Kaufman, D.K. Pai, Musculotendon simulation for hand animation. ACM Trans. Graph.27(3), 83:1–83:8 (2008)Google Scholar
  18. 18.
    M.B. Cline, D.K. Pai, Post-stabilization for rigid body simulation with contact and constraints, in Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3 (2003), pp. 3744–3751Google Scholar
  19. 19.
    R.M. Murray, Z. Li, S.S. Sastry, A Mathematical Introduction to Robotic Manipulation CRC Press, Boca Raton, FL, USA (1994)Google Scholar
  20. 20.
    Y. Remion, J. Nourrit, D. Gillard, Dynamic animation of spline like objects, in Proceedings of WSCG Conference (1999), pp. 426–432Google Scholar
  21. 21.
    F. Zajac, Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Crit. Rev. Biomed. Eng. 17(4), 359–411 (1989)Google Scholar
  22. 22.
    D. Morgan, New insights into the behavior of muscle during active lengthening. Biophys J. 57(2), 209--221 (1990)Google Scholar
  23. 23.
    M. Epstein, W. Herzog, Theoretical Models of Skeletal Muscle (Wiley, New York, 1998)Google Scholar
  24. 24.
    B. Garner, M. Pandy, The obstacle-set method for representing muscle paths in musculoskeletal models. Comput. Methods Biomech. Biomed. Eng. 3(1), 1–30 (2000)CrossRefGoogle Scholar
  25. 25.
    Q. Wei, S. Sueda, D.K. Pai, Physically-based modeling and simulation of extraocular muscles. Prog. Biophys. Mol. Biol. 103(2), 273--283 (2010)Google Scholar
  26. 26.
    I. Stavness, A. Hannam, J. Lloyd, S. Fels, Predicting muscle patterns for hemimandibulectomy models. Comput. Methods Biomech. Biomed. Eng. 13(4), 483–491 (2010)CrossRefGoogle Scholar
  27. 27.
    R.D. Crowninshield, R.A. Brand, A physiologically based criterion of muscle force prediction in locomotion. J. Biomech. 14(11), 793–801 (1981)CrossRefGoogle Scholar
  28. 28.
    W. Herzog, Individual muscle force estimations using a non-linear optimal design. J. Neurosci. Methods 21(2–4), 167–179 (1987)CrossRefGoogle Scholar
  29. 29.
    K.L. Moore, A.F. Dalley, Clinically Oriented Anatomy, 4th edn., ed. by P.J. Kelly (Lippincott Williams & Wilkins, Philadelphia, 1999)Google Scholar
  30. 30.
    S. Sueda, G.L. Jones, D.I.W. Levin, D.K. Pai, Large-scale dynamic simulation of highly constrained strands. ACM Trans. Graph. 30(4), 39:1–39:9 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Sensorimotor Systems Laboratory, Department of Computer ScienceUniversity of British ColumbiaVancouverCanada

Personalised recommendations