“Biomechanical simulation and control of hands and tendinous systems” by Sachdeva, Sueda, Bradley, Fain and Pai

  • ©Prashant Sachdeva, Shinjiro Sueda, Susanne Bradley, Mikhail Fain, and Dinesh K. Pai

Conference:


Type:


Title:

    Biomechanical simulation and control of hands and tendinous systems

Session/Category Title: Modeling, Controlling, and Suturing Humans


Presenter(s)/Author(s):


Moderator(s):



Abstract:


    The tendons of the hand and other biomechanical systems form a complex network of sheaths, pulleys, and branches. By modeling these anatomical structures, we obtain realistic simulations of coordination and dynamics that were previously not possible. First, we introduce Eulerian-on-Lagrangian discretization of tendon strands, with a new selective quasistatic formulation that eliminates unnecessary degrees of freedom in the longitudinal direction, while maintaining the dynamic behavior in transverse directions. This formulation also allows us to take larger time steps. Second, we introduce two control methods for biomechanical systems: first, a general-purpose learning-based approach requiring no previous system knowledge, and a second approach using data extracted from the simulator. We use various examples to compare the performance of these controllers.

References:


    1. Albrecht, I., Haber, J., and Seidel, H.-P. 2003. Construction and animation of anatomically based human hand models. In ACM SIGGRAPH/Eurographics symp. comput. anim., 98–109. Google ScholarDigital Library
    2. Andoni, A., and Indyk, P. 2008. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Communications of the ACM 51 (Jan), 117–122. Google ScholarDigital Library
    3. Andrews, S., and Kry, P. G. 2013. Goal directed multi-finger manipulation: Control policies and analysis. Computers & Graphics 37, 7, 830–839. Google ScholarDigital Library
    4. Bédard, P., and Sanes, J. 2009. Gaze and hand position effects on finger-movement-related human brain activation. J. Neurophysiol. 101, 2 (Feb), 834–842.Google ScholarCross Ref
    5. Bergou, M., Wardetzky, M., Robinson, S., Audoly, B., and Grinspun, E. 2008. Discrete elastic rods. ACM Trans. Graph. 27, 3 (Aug), 63:1–63:12. Google ScholarDigital Library
    6. Bergou, M., Audoly, B., Vouga, E., Wardetzky, M., and Grinspun, E. 2010. Discrete viscous threads. ACM Trans. Graph. 29, 4 (Jul), 116:1–116:10. Google ScholarDigital Library
    7. Blemker, S. S., and Delp, S. L. 2005. Three-dimensional representation of complex muscle architectures and geometries. ANN BIOMED ENG 33, 5 (May), 661–673.Google Scholar
    8. Burridge, R. R., Rizzi, A. A., and Koditschek, D. E. 1999. Sequential Composition of Dynamically Dexterous Robot Behaviors. Int J Robot Res 18, 6 (June), 534–555.Google ScholarCross Ref
    9. Chen, D. T., and Zeltzer, D. 1992. Pump it up: computer animation of a biomechanically based model of muscle using the finite element method. In Computer Graphics (Proc. SIGGRAPH 92), vol. 26, ACM, 89–98. Google ScholarDigital Library
    10. Damsgaard, M., Rasmussen, J., Christensen, S., Surma, E., and Dezee, M. 2006. Analysis of musculoskeletal systems in the AnyBody Modeling System. SIMUL MODEL PRACT TH 14, 8 (Nov.), 1100–111.Google ScholarCross Ref
    11. Delp, S. L., Anderson, F. C., Arnold, A. S., Loan, P., Habib, A., John, C. T., Guendelman, E., and Thelen, D. G. 2007. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54, 11, 1940–1950.Google ScholarCross Ref
    12. Deshpande, A. D., Ko, J., Fox, D., and Matsuoka, Y. 2013. Control strategies for the index finger of a tendon-driven hand. Int J Robot Res 32, 1 (Jan.), 115–128. Google ScholarDigital Library
    13. DiMaio, S., and Salcudean, S. 2002. Needle insertion modelling and simulation. In ICRA, vol. 2, 2098–2105 vol.2.Google Scholar
    14. ElKoura, G., and Singh, K. 2003. Handrix: animating the human hand. In ACM SIGGRAPH/Eurographics symp. comput. anim., 110–119. Google ScholarDigital Library
    15. Epstein, M., and Herzog, W. 1998. Theoretical Models of Skeletal Muscle. John Wiley and Sibs.Google Scholar
    16. Fan, Y., Litven, J., and Pai, D. K. 2014. Active volumetric musculoskeletal systems. ACM Trans. Graph. 33, 4 (July), 152:1–152:9. Google ScholarDigital Library
    17. Fortney, K., and Tweed, D. B. 2012. Computational advantages of reverberating loops for sensorimotor learning. Neural computation 24, 3 (Mar.), 611–34. Google ScholarDigital Library
    18. Garner, B., and Pandy, M. 2000. The obstacle-set method for representing muscle paths in musculoskeletal models. Comput Methods Biomech Biomed Engin 3, 1, 1–30.Google ScholarCross Ref
    19. Geijtenbeek, T., van de Panne, M., and van der Stappen, A. F. 2013. Flexible Muscle-Based Locomotion for Bipedal Creatures. ACM Transactions on Graphics 32, 6. Google ScholarDigital Library
    20. Hou, Z.-G., Gupta, M. M., Nikiforuk, P. N., Tan, M., and Cheng, L. 2007. A Recurrent Neural Network for Hierarchical Control of Interconnected Dynamic Systems. IEEE Transactions on Neural Networks 18, 2 (Mar.), 466–481. Google ScholarDigital Library
    21. Huang, H., Zhao, L., Yin, K., Qi, Y., Yu, Y., and Tong, X. 2011. Controllable hand deformation from sparse examples with rich details. In ACM SIGGRAPH/Eurographics symp. comput. anim., 73–82. Google ScholarDigital Library
    22. Indyk, P., and Motwani, R. 1998. Approximate nearest neighbor: Towards removing the curse of dimensionality. In Proc. STOC, 604–613. Google ScholarDigital Library
    23. Johnson, E., Morris, K., and Murphey, T. 2009. A variational approach to strand-based modeling of the human hand. In Algorithmic Foundation of Robotics VIII, G. Chirikjian, H. Choset, M. Morales, and T. Murphey, Eds., vol. 57 of Springer Tracts in Advanced Robotics. Springer, 151–166.Google Scholar
    24. Kapandji, I. A. 2007. The Physiology of the Joints, Volume 1: Upper Limb, 6 ed. Churchill Livingstone.Google Scholar
    25. Kaufman, K. R., Morrow, D. A., Odegard, G. M., Donahue, T. L. H., Cottler, P. J., Ward, S., and Lieber, R. 2010. 3d model of skeletal muscle to predict intramuscular pressure. In ASB Annual Conference.Google Scholar
    26. Kry, P. G., and Pai, D. K. 2006. Interaction capture and synthesis. ACM Trans. Graph. 25, 3 (Jul), 872–880. Google ScholarDigital Library
    27. Kurihara, T., and Miyata, N. 2004. Modeling deformable human hands from medical images. In ACM SIGGRAPH/ Eurographics symp. comput. anim., 355–363. Google ScholarDigital Library
    28. Lang, C. E., and Schieber, M. H. 2004. Human finger independence: limitations due to passive mechanical coupling versus active neuromuscular control. J. Neurophysiol. 92, 5 (Nov.), 2802–2810.Google ScholarCross Ref
    29. Lee, S.-H., and Terzopoulos, D. 2006. Heads up!: biomechanical modeling and neuromuscular control of the neck. ACM Trans. Graph. 25, 3 (Jul), 1188–1198. Google ScholarDigital Library
    30. Lee, S.-H., Sifakis, E., and Terzopoulos, D. 2009. Comprehensive biomechanical modeling and simulation of the upper body. ACM Trans. Graph. 28, 4 (Sep), 99:1–99:17. Google ScholarDigital Library
    31. Lee, Y., Park, M. S., Kwon, T., and Lee, J. 2014. Locomotion control for many-muscle humanoids. ACM Trans. Graph. 33, 6 (Nov.), 218:1–218:11. Google ScholarDigital Library
    32. Leijnse, J. N., Bonte, J. E., Landsmeer, J. M., Kalker, J. J., Van Der Meulen, J. C., and Snijders, C. J. 1992. Biomechanics of the finger with anatomical restrictions–the significance for the exercising hand of the musician. J. Biomech. 25, 11, 1253–1264.Google ScholarCross Ref
    33. Li, Y., Fu, J. L., and Pollard, N. S. 2007. Data-driven grasp synthesis using shape matching and task-based pruning. IEEE Trans. Vis. Comput. Graphics 13 (July), 732–747. Google ScholarDigital Library
    34. Li, D., Sueda, S., Neog, D. R., and Pai, D. K. 2013. Thin skin elastodynamics. ACM Trans. Graph. (Proc. SIGGRAPH) 32, 4 (July), 49:1–49:9. Google ScholarDigital Library
    35. Liu, C. K. 2008. Synthesis of interactive hand manipulation. In ACM SIGGRAPH/Eurographics symp. comput. anim., 163–171. Google ScholarDigital Library
    36. Liu, C. K. 2009. Dextrous manipulation from a grasping pose. ACM Trans. Graph. 28 (Jul), 59:1–59:6. Google ScholarDigital Library
    37. Malhotra, M., Rombokas, E., Theodorou, E., Todorov, E., and Matsuoka, Y. 2012. Reduced Dimensionality Control for the ACT Hand. In ICRA, IEEE, 5117–5122.Google Scholar
    38. McAdams, A., Zhu, Y., Selle, A., Empey, M., Tamstorf, R., Teran, J., and Sifakis, E. 2011. Efficient elasticity for character skinning with contact and collisions. ACM Trans. Graph. 30, 4 (Jul), 37:1–37:12. Google ScholarDigital Library
    39. Mordatch, I., Popović, Z., and Todorov, E. 2012. Contact-invariant optimization for hand manipulation. In Proceedings of the ACM SIGGRAPH/Eurographics symp. comput. anim., Eurographics Association, 137–144. Google ScholarDigital Library
    40. Ng-Thow-Hing, V. 2001. Anatomically-based models for physical and geometric reconstruction of humans and other animals. PhD thesis, The University of Toronto. Google ScholarDigital Library
    41. Pollard, N. S., and Zordan, V. B. 2005. Physically based grasping control from example. In ACM SIGGRAPH/Eurographics symp. comput. anim., 311–318. Google ScholarDigital Library
    42. Robinson, D., O’meara, D., Scott, A., and Collins, C. 1969. Mechanical components of human eye movements. Journal of Applied Physiology 26, 5, 548–553.Google ScholarCross Ref
    43. Rombokas, E., Malhotra, M., Theodorou, E., Todorov, E., and Matsuoka, Y. 2012. Tendon-Driven Variable Impedance Control Using Reinforcement Learning. In RSS.Google Scholar
    44. Shadmehr, R. 1998. Equilibrium point hypothesis. In The handbook of brain theory and neural networks, MIT Press, 370–372. Google ScholarDigital Library
    45. Sifakis, E., Neverov, I., and Fedkiw, R. 2005. Automatic determination of facial muscle activations from sparse motion capture marker data. ACM Trans. Graph. 24, 3 (Jul), 417–425. Google ScholarDigital Library
    46. Spillmann, J., and Teschner, M. 2008. An adaptive contact model for the robust simulation of knots. Computer Graphics Forum 27, 2, 497–506.Google ScholarCross Ref
    47. Sueda, S., Kaufman, A., and Pai, D. K. 2008. Musculotendon simulation for hand animation. ACM Trans. Graph. 27, 3 (Aug), 83:1–83:8. Google ScholarDigital Library
    48. Sueda, S., Jones, G. L., Levin, D. I. W., and Pai, D. K. 2011. Large-scale dynamic simulation of highly constrained strands. ACM Trans. Graph. 30, 4 (Jul), 39:1–39:9. Google ScholarDigital Library
    49. Teran, J., Blemker, S., Hing, V. N. T., and Fedkiw, R. 2003. Finite volume methods for the simulation of skeletal muscle. In ACM SIGGRAPH/Eurographics symp. comput. anim., 68–74. Google ScholarDigital Library
    50. Teran, J., Sifakis, E., Blemker, S. S., Ng-Thow-Hing, V., Lau, C., and Fedkiw, R. 2005. Creating and simulating skeletal muscle from the visible human data set. IEEE Transactions on Visualization and Computer Graphics 11, 3, 317–328. Google ScholarDigital Library
    51. Tsang, W., Singh, K., and Fiume, E. 2005. Helping hand: an anatomically accurate inverse dynamics solution for unconstrained hand motion. In ACM SIGGRAPH/Eurographics symp. comput. anim., 319–328. Google ScholarDigital Library
    52. Valero-Cuevas, F., Yi, J.-W., Brown, D., McNamara, R., Paul, C., and Lipson, H. 2007. The tendon network of the fingers performs anatomical computation at a macroscopic scale. IEEE Trans. Biomed. Eng. 54, 6, 1161–1166.Google ScholarCross Ref
    53. Wang, J. M., Hamner, S. R., Delp, S. L., and Koltun, V. 2012. Optimizing locomotion controllers using biologically-based actuators and objectives. ACM Trans. Graph. 31, 4 (July), 25:1–25:11. Google ScholarDigital Library
    54. Wang, Y., Min, J., Zhang, J., Liu, Y., Xu, F., Dai, Q., and Chai, J. 2013. Video-based hand manipulation capture through composite motion control. ACM Trans. Graph. 32, 4 (July), 43:1–43:14. Google ScholarDigital Library
    55. Zajac, F. 1989. Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Crit Rev Biomed Eng. 17, 4, 359–411.Google Scholar
    56. Zancolli, E. 1979. Structural and Dynamic Bases of Hand Surgery. Lippincott.Google Scholar
    57. Zhang, A., Malhotra, M., and Matsuoka, Y. 2011. Musical piano performance by the ACT Hand. In IEEE International Conference on Robotics and Automation, IEEE, Shanghai, 3536–3541.Google Scholar
    58. Zhao, W., Zhang, J., Min, J., and Chai, J. 2013. Robust realtime physics-based motion control for human grasping. ACM Trans. Graph. 32, 6 (Nov.), 207:1–207:12. Google ScholarDigital Library
    59. Zhu, Q.-H., Chen, Y., and Kaufman, A. 1998. Real-time biomechanically-based muscle volume deformation using FEM. Computer Graphics Forum 17, 3, 275–284.Google ScholarCross Ref


ACM Digital Library Publication:



Overview Page: