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Implications

  • Francisco J. Valero-Cuevas
Chapter
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 8)

Abstract

This book is deliberately a short introduction to the mathematical and anatomical foundations of neuromechanics. My hope is that you will take these concepts and challenge, modify, extend, and leverage them to advance the science of neuromuscular control and its related areas, such as robotics, musculoskeletal modeling, computational neuroscience, rehabilitation, and evolutionary biology. Having established a common language, conceptual framework, and computational repertoire, I discuss several implications of this neuromechanical perspective. My intent is that my presentation of several issues, research directions, tenets, and debates, however brief, will inspire and encourage you in your research.

Keywords

Model Predictive Control Joint Torque Task Constraint Extensor Digitorum Communis Fingertip Force 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    N.A. Bernstein, The Co-ordination and Regulation of Movements (Pergamon Press, New York, 1967)Google Scholar
  2. 2.
    J.J. Kutch, F.J. Valero-Cuevas, Muscle redundancy does not imply robustness to muscle dysfunction. J. Biomech. 44(7), 1264–1270 (2011)CrossRefGoogle Scholar
  3. 3.
    M.L. Latash, The bliss (not the problem) of motor abundance (not redundancy). Exp. Brain Res. 217(1), 1–5 (2012)Google Scholar
  4. 4.
    G.E. Loeb, Overcomplete musculature or underspecified tasks? Mot. Control 4(1), 81–83 (2000)Google Scholar
  5. 5.
    K.G. Keenan, V.J. Santos, M. Venkadesan, F.J. Valero-Cuevas, Maximal voluntary fingertip force production is not limited by movement speed in combined motion and force tasks. J. Neurosci. 29, 8784–8789 (2009)CrossRefGoogle Scholar
  6. 6.
    F.J. Valero-Cuevas, F.E. Zajac, C.G. Burgar, Large index-fingertip forces are produced by subject-independent patterns of muscle excitation. J. Biomech. 31, 693–703 (1998)CrossRefGoogle Scholar
  7. 7.
    F.J. Valero-Cuevas, H. Hoffmann, M.U. Kurse, J.J. Kutch, E.A. Theodorou, Computational models for neuromuscular function. IEEE Rev. Biomed. Eng. 2, 110–135 (2009)Google Scholar
  8. 8.
    R. Shadmehr, S. Mussa-Ivaldi, Biological Learning and Control: How the Brain Builds Representations, Predicts Events, and Makes Decisions (MIT Press, Cambridge, 2012)Google Scholar
  9. 9.
    E. Todorov, M.I. Jordan, Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5(11), 1226–1235 (2002)CrossRefGoogle Scholar
  10. 10.
    E. Theodorou, E. Todorov, F.J. Valero-Cuevas, Neuromuscular stochastic optimal control of a tendon driven index finger model, in 2011 American Control Conference (ACC) (IEEE, 2011), pp. 348–355Google Scholar
  11. 11.
    E. Theodorou, F.J. Valero-Cuevas, Optimality in neuromuscular systems, in 2010 IEEE Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) (IEEE, 2010), pp. 4510–4516Google Scholar
  12. 12.
    V. Kumar, Y. Tassa, T. Erez, E. Todorov, Real-time behaviour synthesis fordynamic hand-manipulation, in 2014 IEEE International Conference on Robotics and Automation (ICRA), (IEEE, 2014), pp. 6808–6815Google Scholar
  13. 13.
    M. Kalakrishnan, J. Buchli, P. Pastor, M. Mistry, S. Schaal, Learning, planning, and control for quadruped locomotion over challenging terrain. Int. J. Robot. Res. 30(2), 236–258 (2011)Google Scholar
  14. 14.
    E. Theodorou, J. Buchli, S. Schaal, A generalized path integral control approach to reinforcement learning. J. Mach. Learn. Res. 11, 3137–3181 (2010)Google Scholar
  15. 15.
    Wikipedia contributors. Basis vectors. Wikipedia, The Free Encyclopedia. http://en.wikipedia.org/wiki/Curse_of_dimensionality. Accessed 7 June 2015
  16. 16.
    A.D. Kuo, F.E. Zajac, Human standing posture: multi-joint movement strategies based on biomechanical constraints. Prog. Brain Res. 97, 349–358 (1993)CrossRefGoogle Scholar
  17. 17.
    F.J. Cole et al. A History of Comparative Anatomy from Aristotle to the Eighteenth Century (Macmillan Publisher, London, 1944)Google Scholar
  18. 18.
    A. Vesalius, De Humani Corporis Fabrica Libri Septem (Ex officina I. Oporini, Basileae, 1543)Google Scholar
  19. 19.
    R. Van Rijn, The Anatomy Lesson of Dr. Nicolaes Tulp (1632)Google Scholar
  20. 20.
    F.J. Valero-Cuevas, C.F. Small, Load dependence in carpal kinematics during wrist flexion in vivo. Clin. Biomech. 12, 154–159 (1997)CrossRefGoogle Scholar
  21. 21.
    H. van Duinen, S.C. Gandevia, Constraints for control of the human hand. J. Physiol. 589(23), 5583–5593 (2011)CrossRefGoogle Scholar
  22. 22.
    C.E. Wall, A model of temporomandibular joint function in anthropoid primates basedon condylar movements during mastication. Am. J. Phys. Anthropol. 109(1), 67–88 (1999)CrossRefGoogle Scholar
  23. 23.
    F.J. Valero-Cuevas, Predictive modulation of muscle coordination pattern magnitude scales fingertip force magnitude over the voluntary range. J. Neurophysiol. 83(3), 1469–1479 (2000)Google Scholar
  24. 24.
    F.J. Valero-Cuevas, J.D. Towles, V.R. Hentz, Quantification of fingertip force reduction in the forefinger following simulated paralysis of extensor and intrinsic muscles, J. Biomech. 33, 1601–1609 (2000)Google Scholar
  25. 25.
    L. Gregoire, H.E. Veeger, P.A. Huijing, S.G.J. van Ingen, Role of mono-and biarticular muscles in explosive movements. Int. J. Sport. Med. 5(6):301–305, (1984)Google Scholar
  26. 26.
    J.M. Inouye, F.J. Valero-Cuevas, Anthropomorphic tendon-driven robotic hands can exceed human grasping capabilities following optimization. Int. J. Robot. Res. (2013)Google Scholar
  27. 27.
    F.J. Valero-Cuevas, J.W. Yi, D. Brown, R.V. McNamara, C. Paul, H. Lipson, The tendon network of the fingers performs anatomical computation at a macroscopic scale. IEEE Trans. Biomed. Eng. 54, 1161–1166 (2007)CrossRefGoogle Scholar
  28. 28.
    V.S. Chib, M.A Krutky, K.M. Lynch, F.A. Mussa-Ivaldi, The separate neural control of hand movements and contact forces. J. Neurosci. 29(12), 3939–3947 (2009)Google Scholar
  29. 29.
    R.M. Murray, Z. Li, S.S. Sastry, A Mathematical Introduction to Robotic Manipulation (CRC Press, Florida, 1994)Google Scholar
  30. 30.
    V. Squeri, L. Masia, M. Casadio, P. Morasso, E. Vergaro, Force-field compensation in a manual tracking task. PLoS One 5(6), e11189 (2010)Google Scholar
  31. 31.
    M. Venkadesan, F.J. Valero-Cuevas, Neural control of motion-to-force transitions with the fingertip. J. Neurosci. 28, 1366–1373 (2008)CrossRefGoogle Scholar
  32. 32.
    T. Yoshikawa, Foundations of Robotics: Analysis and Control (MIT Press, Cambridge, 1990)Google Scholar
  33. 33.
    N. Hogan, Adaptive control of mechanical impedance by coactivation of antagonist muscles. IEEE Trans. Autom. Control 29(8), 681–690 (1984)MATHCrossRefGoogle Scholar
  34. 34.
    E.R. Kearney, I.W. Hunter, System identification of human joint dynamics. Crit. Rev. Biomed. Eng. 18(1), 55–87 (1989)Google Scholar
  35. 35.
    J.M. Lanman, Movement and the mechanical properties of the intact human elbow joint. Ph.D. thesis, Massachusetts Institute of Technology (1980)Google Scholar
  36. 36.
    G.I. Zahalak, S.J. Heyman, A quantitative evaluation of the frequency-response characteristics of active human skeletal muscle in vivo. J. Biomech. Eng. 101(1), 28–37 (1979)Google Scholar
  37. 37.
    E. Burdet, R. Osu, D.W. Franklin, T.E. Milner, M. Kawato, The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature 414(6862), 446–449 (2001)CrossRefGoogle Scholar
  38. 38.
    E. Burdet, R. Osu, D.W. Franklin, T. Yoshioka, T.E. Milner, M. Kawato, A method for measuring endpoint stiffness during multi-joint arm movements. J. Biomech. 33(12), 1705–1709 (2000)CrossRefGoogle Scholar
  39. 39.
    M. Darainy, N. Malfait, P.L. Gribble, F. Towhidkhah, D.J. Ostry, Learning to control arm stiffness under static conditions. J. Neurophysiol. 92(6), 3344 (2004)CrossRefGoogle Scholar
  40. 40.
    T. Flash, F. Mussa-Ivaldi, Human arm stiffness characteristics during the maintenance of posture. Exp. Brain Res. 82(2), 315–326 (1990)CrossRefGoogle Scholar
  41. 41.
    D.W. Franklin, G. Liaw, T.E. Milner, R. Osu, E. Burdet, M. Kawato, Endpoint stiffness of the arm is directionally tuned to instability in the environment. J. Neurosci. 27(29), 7705–7716 (2007)CrossRefGoogle Scholar
  42. 42.
    D.W. Franklin, U. So, M. Kawato, T.E. Milner, Impedance control balances stability with metabolically costly muscle activation. J. Neurophysiol. 92(5), 3097 (2004)CrossRefGoogle Scholar
  43. 43.
    H. Gomi, R. Osu, Task-dependent viscoelasticity of human multijoint arm and its spatial characteristics for interaction with environments. J. Neurosci. 18(21), 8965–8978 (1998)Google Scholar
  44. 44.
    N. Hogan, Impedance control: an approach to manipulation, in American Control Conference (IEEE, 1984), pp. 304–313Google Scholar
  45. 45.
    N. Hogan, The mechanics of multi-joint posture and movement control. Biol. Cybern. 52(5), 315–331 (1985)MATHMathSciNetCrossRefGoogle Scholar
  46. 46.
    X. Hu, W.M. Murray, E.J. Perreault, Muscle short-range stiffness can be used to estimate the endpoint stiffness of the human arm. J. Neurophysiol. 105(4), 1633–1641 (2011)CrossRefGoogle Scholar
  47. 47.
    H.U. Xiao, W.M. Murray, E.J. Perreault, Biomechanical constraints on the feedforward regulation of endpoint stiffness. J. Neurophysiol. 108(8), 2083–2091 (2012)Google Scholar
  48. 48.
    A. Kadiallah, G. Liaw, M. Kawato, D.W. Franklin, E. Burdet. Impedance control is selectively tuned to multiple directions of movement. J. NeurophysiolGoogle Scholar
  49. 49.
    J. McIntyre, F.A. Mussa-Ivaldi, E. Bizzi, The control of stable postures in the multijoint arm. Exp. Brain Res. 110(2), 248–264 (1996)CrossRefGoogle Scholar
  50. 50.
    T.E. Milner, Contribution of geometry and joint stiffness to mechanical stability of the human arm. Exp. Brain Res. 143(4), 515–519 (2002)CrossRefGoogle Scholar
  51. 51.
    F.A. Mussa-Ivaldi, N. Hogan, E. Bizzi, Neural, mechanical, and geometric factors subserving arm posture in humans. J. Neurosci. 5(10), 2732 (1985)Google Scholar
  52. 52.
    R. Osu, H. Gomi, Multijoint muscle regulation mechanisms examined by measured human arm stiffness and EMG signals. J. Neurophysiol. 81(4), 1458 (1999)Google Scholar
  53. 53.
    E.J. Perreault, R.F. Kirsch, P.E. Crago, Effects of voluntary force generation on the elastic components of endpoint stiffness. Exp. Brain Res. (Experimentelle Hirnforschung Experimentation cerebrale) 141(3), 312, (2001)Google Scholar
  54. 54.
    E.J. Perreault, R.F. Kirsch, P.E. Crago, Voluntary control of static endpoint stiffness during force regulation tasks. J. Neurophysiol. 87(6), 2808 (2002)Google Scholar
  55. 55.
    D. Shin, J. Kim, Y. Koike, A myokinetic arm model for estimating joint torque and stiffness from EMG signals during maintained posture. J. Neurophysiol. 101(1), 387–401 (2009)CrossRefGoogle Scholar
  56. 56.
    S. Stroeve, Impedance characteristics of a neuromusculoskeletal model of the human arm i. Posture control. Biol. Cybern. 81(5), 475–494 (1999)MATHCrossRefGoogle Scholar
  57. 57.
    K.P. Tee, D.W. Franklin, M. Kawato, T.E. Milner, E. Burdet, Concurrent adaptation of force and impedance in the redundant muscle system. Biol. Cybern. 102(1), 31–44 (2010)Google Scholar
  58. 58.
    J.M. Inouye, F.J. Valero-Cuevas, A novel computational approach helps explain and reconcile conflicting experimental findings on the neural control of arm endpoint stiffness, in 2012 22nd Annual Society for the Neural Control of Movement Conference (Venice, Italy, 2012)Google Scholar
  59. 59.
    C. Tomberg, M.D. Caramia, Prime mover muscle in finger lift or finger flexion reaction times: identification with transcranial magnetic stimulation. Electroencephalogr. Clin. Neurophysiol. Evoked Potentials Sect. 81(4), 319–322 (1991)Google Scholar
  60. 60.
    T.E. Milner, Adaptation to destabilizing dynamics by means of muscle cocontraction. Exp. Brain Res. 143(4), 406–416 (2002)Google Scholar
  61. 61.
    F.J. Valero-Cuevas, An integrative approach to the biomechanical function and neuromuscular control of the fingers. J. Biomech. 38, 673–684 (2005)CrossRefGoogle Scholar
  62. 62.
    R. Balasubramanian, Y. Matsuoka, Biological stiffness control strategies for the anatomically correct testbed (act) hand, in 2008 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2008), pp. 737–742Google Scholar
  63. 63.
    J.J. Kutch, F.J. Valero-Cuevas, Challenges and new approaches to proving the existence of muscle synergies of neural origin. PLoS Comput. Biol. 8(5), e1002434 (2012)CrossRefGoogle Scholar
  64. 64.
    M.K. Steele, M.C. Tresch, E.J. Perreault, Consequences of biomechanically constrained tasks in the design and interpretation of synergy analyses. J. Neurophysiol. 113(7), 2102–2113 (2015)Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringThe University of Southern CaliforniaLos AngelesUSA
  2. 2.Division of Biokinesiology and Physical TherapyThe University of Southern CaliforniaLos AngelesUSA

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