Skip to main content
SpringerLink
Log in

FES Modeling

  • Chapter
  • First Online:
Neuro-fuzzy Modeling of Multi-field Surface Neuroprostheses for Hand Grasping

Part of the book series: Springer Theses ((Springer Theses))

  • 254 Accesses

Abstract

Transcutaneous FES systems are complex. They can be divided into smaller subsystems that can be modeled separately and in combinations. Most transcutaneous FES models that relate stimulation parameters with joint dynamics or kinematics are based on the combination of some of the models described below and adaptations of these for particular FES applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. J.P. Reilly, Applied Bioelectricity: From Electrical Stimulation to Electropathology (Springer Science & Business Media, Berlin, 1998)

    Google Scholar 

  2. A. Van Boxtel, Skin resistance during square-wave electrical pulses of 1–10 mA. Med. Biol. Eng. Comput. 15, 679–687 (1977)

    Article  Google Scholar 

  3. S.J. Dorgan, R.B. Reilly, A model for human skin impedance during surface functional neuromuscular stimulation. IEEE Trans. Rehabil. Eng. 7, 341–348 (1999)

    Article  Google Scholar 

  4. A. Kuhn, T. Keller, B. Prenaj, M. Morari, The relevance of nonlinear skin properties for a transcutaneous electrical stimulation model, in 11th Annual Conference of the International Functional Electrical Stimulation Society (IFESS), Zao, Japan (2006)

    Google Scholar 

  5. L. Mesin, R. Merletti, Distribution of electrical stimulation current in a planar multilayer anisotropic tissue. IEEE Trans. Biomed. Eng. 55, 660–670 (2008)

    Article  Google Scholar 

  6. L.M. Livshitz, J. Mizrahi, P.D. Einziger, Interaction of array of finite electrodes with layered biological tissue: effect of electrode size and configuration. IEEE Trans. Neural Syst. Rehabil. Eng. 9, 355–361 (2001)

    Article  Google Scholar 

  7. A. Kuhn, T. Keller, M. Lawrence, M. Morari, A model for transcutaneous current stimulation: simulations and experiments. Med. Biol. Eng. Comput. 47, 279–289 (2009)

    Article  Google Scholar 

  8. J. Martinek, M. Reichel, F. Rattay, W. Mayr, Analysis of calculated electrical activation of denervated muscle fibers in the human thigh. Artif. Organs 29, 444–447 (2005)

    Article  Google Scholar 

  9. Y. Stickler, J. Martinek, C. Hofer, F. Rattay, A finite element model of the electrically stimulated human thigh: changes due to denervation and training. Artif. Organs 32, 620–624 (2008)

    Article  Google Scholar 

  10. A. Kuhn, Modeling transcutaneous electrical stimulation, Ph.D. thesis, ETH Zurich, 2008

    Google Scholar 

  11. F. Rattay, Electrical Nerve Stimulation (Springer, Wien, 1990)

    Book  Google Scholar 

  12. M. Moffitt, C.C. McIntyre, W.M. Grill, Prediction of myelinated nerve fiber stimulation thresholds: limitations of linear models. IEEE Trans. Biomed. Eng. 51, 229–236 (2004)

    Article  Google Scholar 

  13. A.L. Hodgkin, A.F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952)

    Article  Google Scholar 

  14. B. Frankenhaeuser, A.F. Huxley, The action potential in the myelinated nerve fibre of Xenopus laevis as computed on the basis of voltage clamp data. J. Physiol. 171, 302–315 (1964)

    Article  Google Scholar 

  15. S.Y. Chiu, J.M. Ritchie, R.B. Rogart, D. Stagg, A quantitative description of membrane currents in rabbit myelinated nerve. J. Physiol. 292, 149–166 (1978)

    Article  Google Scholar 

  16. C.C. McIntyre, A.G. Richardson, W.M. Grill, Modeling the excitability of mammalian nerve fibers: influence of afterpotentials on the recovery cycle. J. Neurophysiol. 87, 995–1006 (2002)

    Article  Google Scholar 

  17. A.V. Hill, The heat of shortening and the dynamic constants of muscle. Proc. R. Soc. Lond. B Biol. Sci. 126, 136–195 (1938)

    Article  Google Scholar 

  18. C.Y. Scovil, J.L. Ronsky, Sensitivity of a Hill-based muscle model to perturbations in model parameters. J. Biomech. 39, 2055–2063 (2006)

    Article  Google Scholar 

  19. A.S. Wexler, J. Ding, S. Binder-Macleod, A mathematical model that predicts skeletal muscle force. IEEE Trans. Biomed. Eng. 44, 337–348 (1997)

    Article  Google Scholar 

  20. J. Ding, A.S. Wexler, S.A. Binder-Macleod, A mathematical model that predicts the force? frequency relationship of human skeletal muscle. Muscle & Nerve 26, 477–485 (2002)

    Article  Google Scholar 

  21. J. Bobet, R.B. Stein, A simple model of force generation by skeletal muscle during dynamic isometric contractions. IEEE Trans. Biomed. Eng. 45, 1010–1016 (1998)

    Article  Google Scholar 

  22. J. Bobet, E.R. Gossen, R.B. Stein, A comparison of models of force production during stimulated isometric ankle dorsiflexion in humans. IEEE Trans. Neural Syst. Rehabil. Eng. 13, 444–451 (2005)

    Article  Google Scholar 

  23. L.A. Frey Law, R.K. Shields, Mathematical models use varying parameter strategies to represent paralyzed muscle force properties: a sensitivity analysis. J. Neuroeng. Rehabil. 2, 12–29 (2005)

    Google Scholar 

  24. L.A. Frey Law, R.K. Shields, Mathematical models of human paralyzed muscle after long-term training. J. Biomech. 40, 2587–2595 (2007)

    Article  Google Scholar 

  25. E.J. Perreault, C.J. Heckman, T.G. Sandercock, Hill muscle model errors during movement are greatest within the physiologically relevant range of motor unit firing rates. J. Biomech. 36, 211–218 (2003)

    Article  Google Scholar 

  26. I.W. Hunter, M.J. Korenberg, The identification of nonlinear biological systems: Wiener and Hammerstein cascade models. Biol. Cybern. 55, 135–144 (1986)

    MathSciNet  MATH  Google Scholar 

  27. K.J. Hunt, M. Munih, N.D. Donaldson, F. Barr, Investigation of the Hammerstein hypothesis in the modeling of electrically stimulated muscle. IEEE Trans. Biomed. Eng. 45, 998–1009 (1998)

    Article  Google Scholar 

  28. E.W. Bai, Z. Cai, S. Dudley-Javorosk, R.K. Shields, Identification of a modified Wiener–Hammerstein system and its application in electrically stimulated paralyzed skeletal muscle modeling. Automatica 45, 736–743 (2009)

    Article  MathSciNet  Google Scholar 

  29. G. Rau, C. Disselhorst-Klug, R. Schmidt, Movement biomechanics goes upwards: from the leg to the arm. J. Biomech. 33, 1207–1216 (2000)

    Article  Google Scholar 

  30. R. Schmidt, C. Disselhorst-Klug, J. Silny, G. Rau, A marker-based measurement procedure for unconstrained wrist and elbow motions. J. Biomech. 32, 615–621 (1999)

    Article  Google Scholar 

  31. E.V. Biryukova, A. Roby-Brami, A.A. Frolov, M. Mokhtari, Kinematics of human arm reconstructed from spatial tracking system recordings. J. Biomech. 33, 985–995 (2000)

    Article  Google Scholar 

  32. B. Hingtgen, J.R. McGuire, M. Wang, G.F. Harris, An upper extremity kinematic model for evaluation of hemiparetic stroke. J. Biomech. 39, 681–688 (2006)

    Article  Google Scholar 

  33. B.A. Garner, M.G. Pandy, Musculoskeletal model of the upper limb based on the visible human male dataset. Comput. Methods Biomech. Biomed. Eng. 4, 93–126 (2001)

    Article  Google Scholar 

  34. A.A. Nikooyan, H.E.J. Veeger, E.K.J. Chadwick, M. Praagman, F.C. van der Helm, Development of a comprehensive musculoskeletal model of the shoulder and elbow. Med. Biol. Eng. Comput. 49, 1425–1435 (2011)

    Article  Google Scholar 

  35. J. Ambrosio, C. Quental, B. Pilarczyk, J. Folgado, J. Monteiro, Multibody biomechanical models of the upper limb, in 2011 Symposium on Human Body Dynamics, vol. 2 (2011), pp. 4–17

    Article  Google Scholar 

  36. K.R. Holzbaur, W.M. Murray, S.L. Delp, A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control. Ann. Biomed. Eng. 33, 829–840 (2005)

    Article  Google Scholar 

  37. P. Cerveri, E. De Momi, N. Lopomo, G. Baud-Bovy, R.M.L. Barros, G. Ferrigno, Finger kinematic modeling and real-time hand motion estimation. Ann. Biomed. Eng. 35, 1989–2002 (2007)

    Article  Google Scholar 

  38. P. Cerveri, N. Lopomo, A. Pedotti, G. Ferrigno, Derivation of centers and axes of rotation for wrist and fingers in a hand kinematic model: methods and reliability results. Ann. Biomed. Eng. 33, 402–412 (2005)

    Article  Google Scholar 

  39. A. Gustus, G. Stillfried, J. Visser, H. Jorntell, P. van der Smagt, Human hand modelling: kinematics, dynamics, applications. Biol. Cybern. 106, 741–755 (2012)

    Article  MathSciNet  Google Scholar 

  40. L. Vigouroux, F. Quaine, A. Labarre-Vila, F. Moutet, Estimation of finger muscle tendon tensions and pulley forces during specific sport-climbing grip techniques. J. Biomech. 39, 2583–2592 (2006)

    Article  Google Scholar 

  41. I. Roloff, V.R. Schoffl, L. Vigouroux, F. Quaine, Biomechanical model for the determination of the forces acting on the finger pulley system. J. Biomech. 39, 915–923 (2006)

    Article  Google Scholar 

  42. J.L. Sancho-Bru, A. Perez-Gonzalez, M. Vergara-Monedero, D. Giurintano, A 3-D dynamic model of human finger for studying free movements. J. Biomech. 34, 1491–1500 (2001)

    Article  Google Scholar 

  43. 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)

    Article  Google Scholar 

  44. F.J. Valero-Cuevas, M.E. Johanson, J.D. Towles, Towards a realistic biomechanical model of the thumb: the choice of kinematic description may be more critical than the solution method or the variability/uncertainty of musculoskeletal parameters. J. Biomech. 36, 1019–1030 (2003)

    Article  Google Scholar 

  45. F.J. Valero-Cuevas, An integrative approach to the biomechanical function and neuromuscular control of the fingers. J. Biomech. 38, 673–684 (2005)

    Article  Google Scholar 

  46. K.S. Fok, S.M. Chou, Development of a finger biomechanical model and its considerations. J. Biomech. 43, 701–713 (2010)

    Article  Google Scholar 

  47. J.L. Sancho-Bru, A. Perez-Gonzalez, M. Vergara, D.J. Giurintano, A 3D biomechanical model of the hand for power grip. J. Biomech. Eng. 125, 78–83 (2003)

    Article  Google Scholar 

  48. J.L. Sancho-Bru et al., Towards a realistic and self-contained biomechanical model of the hand, in Theoretical Biomechanics, ed. by V. Klika (InTech, 2011), pp. 211–240, http://www.intechopen.com/books/theoretical-biomechanics/towards-a-realistic-and-self-contained-biomechanical-model-of-the-hand

  49. R.F. Kirsch, A.M. Acosta, Model-based development of neuroprostheses for restoring proximal arm function. J. Rehabil. Res. Dev. 38, 619–626 (2001)

    Google Scholar 

  50. D. Blana, J.G. Hincapie, E.K. Chadwick, R.F. Kirsch, A musculoskeletal model of the upper extremity for use in the development of neuroprosthetic systems. J. Biomech. 41, 1714–1721 (2008)

    Article  Google Scholar 

  51. C.T. Freeman, A.M. Hughes, J.H. Burridge, P.H. Chappell, P.L. Lewin, E. Rogers, A model of the upper extremity using FES for stroke rehabilitation. J. Biomech. Eng. 131, 031011 (2009)

    Article  Google Scholar 

  52. F. Le, I. Markovsky, C.T. Freeman, E. Rogers, Recursive identification of Hammerstein systems with application to electrically stimulated muscle. Control Eng. Pract. 20, 386–396 (2012)

    Article  Google Scholar 

  53. T. Keller, B. Hackl, M. Lawrence, A. Kuhn, Identification and control of hand grasp using multi-channel transcutaneous electrical stimulation, in 11th Annual Conference of the International Functional Electrical Stimulation Society, Zao, Japan (2006)

    Google Scholar 

  54. A.J. Westerveld, A.C. Schouten, P.H. Veltink, H. van der Kooij, Control of thumb force using surface functional electrical stimulation and muscle load sharing. J. Neuroeng. Rehabil. 10, 104–115 (2013)

    Article  Google Scholar 

  55. A. Soska, C. Freeman, E. Rogers, ILC for FES-based stroke rehabilitation of hand and wrist, in IEEE International Symposium on Intelligent Control (ISIC), Dubrovnik, Croatia (2012)

    Google Scholar 

  56. C.T. Freeman, Electrode array-based electrical stimulation using ILC with restricted input subspace. Control Eng. Pract. 23, 32–43 (2014)

    Article  Google Scholar 

  57. M.W. Coppieters, A.M. Alshami, Longitudinal excursion and strain in the median nerve during novel nerve gliding exercises for carpal tunnel syndrome. J. Orthop. Res. 25, 972–980 (2007)

    Article  Google Scholar 

  58. T.W. Wright, F. Glowczewskie, D. Cowin, D.L. Wheeler, Radial nerve excursion and strain at the elbow and wrist associated with upperextremity motion. J. Hand Surg. 30, 990–996 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Automatic Control and Systems Engineering Department, Engineering School of Bilbao, University of the Basque Country (UPV/EHU), Bilbao, Vizcaya, Spain

    Eukene Imatz Ojanguren

Authors
  1. Eukene Imatz Ojanguren
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eukene Imatz Ojanguren .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Imatz Ojanguren, E. (2019). FES Modeling. In: Neuro-fuzzy Modeling of Multi-field Surface Neuroprostheses for Hand Grasping. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-030-02735-3_6

Download citation

Publish with us

Policies and ethics

Search

Navigation