Control of Rhythmic Movements Using FNS

  • Srinath P. Jayasundera
  • James J. Abbas
  • Peter H. Veltink


It is widely accepted that neural systems for controlling cyclic movements such as walking, running, chewing, and breathing utilize pattern generating neural circuitry. A network of coupled pattern generator units may be used to control such movements in multisegmented skeletal systems. In the work presented here, we use a network of coupled pattern generators as one component in a control system for generating movements using electrical stimulation. The control system is intended for eventual use in Functional Neuromuscular Stimulation (FNS) systems to restore locomotor function to individuals with neurological disorders. Results of computer simulation studies are presented to demonstrate that: (1) the pattern generator component can generate oscillatory patterns over a wide range of model parameter values; (2) the coupling amongst individual pattern generator components can be automatically adjusted to generate specified intersegmental phase lags; and (3) the control system can automatically adjust stimulation parameters to generate a specified movement of a multi-segmented skeletal system.


Central Pattern Generator Functional Electrical Stimulation Rhythmic Movement Neural Network Control Stimulation Pattern 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abbas, J.J. (1996). Using neural models in the design of a movement control system. In Computing Neuroscience. Bower, J. (ed.), pp. 305–310. Academic Press, New York.Google Scholar
  2. Abbas, J.J. and Chizeck, H.J. (1994). Phase-dependent reflexes in a neural network control system. Proc. Southern Biomed. Conf., pp 494–497.Google Scholar
  3. Abbas, J.J. and Chizeck, H.J. (1995). Neural network control of functional neuromuscular stimulation systems: computer simulation studies. IEEE Trans. Biomed. Eng., 42(11):1117–1127.PubMedCrossRefGoogle Scholar
  4. Abbas, J.J. and Triolo R.J. (1993). Experimental evaluation of an adaptive feedforward controller for use in functional neuromuscular stimulation systems. Proc. IEEE/EMBS Conf., 15:1326–1327.Google Scholar
  5. Andrews, B.J., Barnett, R.W., Phillips, G.F., Kirkwood, C.A., Donaldson, N., Rushton, D.N., and Perkins, T.A. (1989). Rule-based control of a hybrid FES orthosis for assisting paraplegic locomotion. Automedica, 11:175–199.Google Scholar
  6. Bajd, T., Andrews, B.J., Kralj, A., and Katakis, J. (1985). Restoration of walking in patients with incomplete spinal cord injuries by use of surface electrical stimulation—preliminary results. Prosthetics Orthotics Intl., 9:109–111.Google Scholar
  7. Beer, R.D., Ritzmann, R.E., and McKenna, T. (1993). Biological neural networks in invertebrate neuroethology and robotics. Academic Press, Boston.Google Scholar
  8. Brodin, L., Traven, H.G.C, Lansner, A., Wallen, P., Ekeberg, O. and Grillner, A. (1991). Computer simulations of N-Methyl-D-Aspartate receptor-induced membrane properties in a neuron model J. Neurophysiol., 66(2):473–483.PubMedGoogle Scholar
  9. Buchanan, J.T. (1992). Neural network simulations of coupled locomotor oscillators in the lamprey spinal cord. Biol. Cybern., 66:367–374.PubMedCrossRefGoogle Scholar
  10. Chizeck, H.J., Kobetic, R., Marsolais, E.B., Abbas, J.J., Donner, I.H., and Simon, E. (1988). Control of Functional Neuromuscular Stimulation Systems for Standing and Locomotion in Paraplegics. Proc. IEEE, 76(9):1155–1165.CrossRefGoogle Scholar
  11. Cohen, A., Ermentrout, G., Kiemel, T., Kopell, N., Sigvardt, K., and Williams, T. (1992). Modeling of intersegmental coordination in the lamprey central pattern generator for locomotion. TINS, 15(11):434–438.PubMedGoogle Scholar
  12. Cohen, A.H. (1994). Evolution of the vertebrates central pattern generator for locomotion. In Neural Control of Rhythmic Movements in Vertebrates. Cohen, A.H., Rossignol, S., and Grillner, S. (eds.), pp. 129–166. John Wiley & Sons, New York.Google Scholar
  13. Crago, P.E., Lan, N., Veltink, P.H., Abbas, J.J., and Kantor, C. (1996). New Control strategies for neuroprosthetic Systems. J. Rehab. Res. Dev., 33(2):158–172.Google Scholar
  14. Cruse, H., Bartling, C., Cymbalyuk, G., Dean, J., and Dreifert, M. (1995). A modular artificial neural net for controlling a six-legged walking system. Biol. Cybern., 72:421–430.PubMedCrossRefGoogle Scholar
  15. Ekeberg, O., Wallen, P., Lansner, A., Traven, H., Brodin, L., and Grillner, S. (1991). A computer based model for realistic simulations of neural networks; I. The single neuron and synaptic interaction. Biol. Cybern., 65:81–90.PubMedCrossRefGoogle Scholar
  16. Ermentrout, B. (1994). XPPAUT1.2-the differential equation tool, (
  17. Ermentrout, B., and Kopell, N. (1994). Learning of phase lags in coupled neural oscillators. SIAM J. Appl. Math., 54(2):478–507.CrossRefGoogle Scholar
  18. Franken, H.M., Veltink, P.H., and Boom, H.B.K. (1994). Restoration of paraplegic gait by functional electrical stimulation. IEEE/EMBS Magazine.Google Scholar
  19. Franken, H.M., Veltink, P.H., Baardman, G., Redmeijer, R.A., and Boom, H.B.K. (1995). Cycle-to-cycle control of swing phase of paraplegic gait induced by surface electrical stimulation. Med. Biol. Eng. Comput., 33:440–451.PubMedCrossRefGoogle Scholar
  20. Graupe, D. and Kohn, K.H. (1994). Functional Electrical Stimulation for Ambulation by Paraplegics. Krieger Publishing Co., Melbourne, Florida.Google Scholar
  21. Graupe, D. and Kordylewski, H. (1995). Artificial neural network control of FES in paraplegics for patient responsive ambulation. Trans. Biomed. Eng., 42(7):699–707.CrossRefGoogle Scholar
  22. Grillner, S. (1981). Control of locomotion in bipeds, tetrapods and fish. In Handbook of Physiology, Sect. 1: The Nervous System II, Motor Control. Brooks, V.B. (ed.), pp. 1179–1236. American Physiological Society, Waverly Press, Bethesda, Maryland.Google Scholar
  23. Grillner, S. and Dubuc, R. (1988). Control of locomotion in vertebrates: spinal and supraspinal mechanisms. Adv. Neurol., 47:425–453.PubMedGoogle Scholar
  24. Grillner, S., Wallen, P., Dale, N., Brodin, L., Buchanan, J., and Hill, R. (1987). Transmitters, membrane properties and network circuitry in the control of locomotion in lamprey TINS, 10:34–41.Google Scholar
  25. Harwell, M.S., Oderkerk, B.J., Sacher, C.A., and Inbar, G.F. (1991). The development of a model reference adaptive controller to control the knee joint of paraplegics. IEEE Trans. Auto. Cont., 36:683–691.CrossRefGoogle Scholar
  26. Hausdorff, J.M. and Durfee, W.K. (1991). Open-loop position control of the knee joint using electrical stimulation of the quadriceps and hamstrings. Med. Biol. Eng. Comput., 29:269–280.PubMedCrossRefGoogle Scholar
  27. Jaeger R. (1992). Lower extremity applications of functional neuromuscular stimulation. Assist. Technol., 4:19–30.PubMedCrossRefGoogle Scholar
  28. Janknegt, R.A., Baeten, C.G.M.I., Weil, E.H., and Spaans, F. (1992). Electrically stimulated gracilis sphincter for treatment of bladder sphincter incontinence. Lancet, 340:1129–1130.PubMedCrossRefGoogle Scholar
  29. Jung, R., Kiemel, T., and Cohen, A.H. (1996). Dynamical behavior of a neural network model of locomotor control in the lamprey. J. Neurophysiol., 75(3):1074–1086.PubMedGoogle Scholar
  30. Kilgore, K.L., Peckham, P.H., Thrope, G.B., and Keith, M.W. (1989). Synthesis of hand movement using functional neuromuscular stimulation. IEEE Trans. Biomed. Eng., 36(7):761–770.PubMedCrossRefGoogle Scholar
  31. Kobetic, R. and Marsolais, E.B. (1985). Automated electrically induced paraplegic gait. Proc. 38th ACEMB, 293.Google Scholar
  32. Kobetic, R. and Marsolais, E.B. (1994). Synthesis of paraplegic gait with multichannel functional neuromuscular stimulation. IEEE Trans. Rehab. Eng., 2(2):66–79.CrossRefGoogle Scholar
  33. Kostov, A., Andrews, B.J., Popovic, D.B., Stein, R.B., and Armstrong, W.W. (1995). Machine learning in control of functional neuromuscular stimulation systems for locomotion. IEEE Trans. BME, 42(6):541–551.CrossRefGoogle Scholar
  34. Kralj, A. and Bajd, T. (1989). Functional Electrical Stimulation: Standing and Walking After Spinal Cord Injury. CRC Press, Boca Raton, Florida.Google Scholar
  35. Marder, E., Abbott, L.F., Buchholtz, F., and Epstein, I.R. (1993). Physiological insights from cellular and network models of the stomatogastric nervous system of lobsters and crabs. American Zoologist, 33(1):29–39.Google Scholar
  36. Marsolais, E.B. and Kobetic, R. (1987). Functional electrical stimulation for walking in paraplegia. J. Bone Joint Surg., 69-A(5):728–733.Google Scholar
  37. Mortimer, J.T. (1981). Motor prostheses. In Handbook of Physiology, Section I: The Nervous System. Brooks, V.B. (ed.), Vol. 2, pp. 155–187. American Physiological Society, Bethesda, Maryland.Google Scholar
  38. Pearson, K., Ramirez, J., and Jiang, W. (1992). Entrainment of the locomotor rhythm by group Ib afferents from ankle extensor muscles in spinal cats. Exp. Brain Res., 90:557–566.PubMedCrossRefGoogle Scholar
  39. Peckham, P.H. (1987). Functional electrical stimulation: current status and future prospects of applications to the neuromuscular system in spinal cord injury. Paraplegia, 25:279–288.PubMedCrossRefGoogle Scholar
  40. Popovic, D.B. (1992). Functional electrical stimulation for lower extremities. In Neural Prostheses: Replacing Motor Function After Disease or Disability. Stein, R.B., Peckham, P.H., and Popovic, D.P. (eds.), pp. 233–251. Oxford University Press, New York.Google Scholar
  41. Sepulveda, F., and Cliquet, Jr., A. (1995). An Artificial neural system for closed loop control of locomotion produced via neuromuscular electrical stimulation. Artif. Organs, 19(3):231–237.PubMedCrossRefGoogle Scholar
  42. Sigvardt, K.A. (1993). Intersegmental coordination in the lamprey central pattern generator for locomotion. Neurosciences, 5:3–15.Google Scholar
  43. Sigvardt, K.A. and Williams, T.L. (1992). Models of central pattern generators as oscillators: the lamprey locomotion CPG. Semi. Neurosci., 4:37–46.CrossRefGoogle Scholar
  44. Skelly, M.M., Chizeck, H.J., and Ferencz, D.C. (1994). Pattern switching and a fuzzy rule base for control of paraplegic walking, Proc. Neural Prosthesis: Motor Systems IV, Mt. Sterling, Ohio, p. 34.Google Scholar
  45. Skinner, S.K., Turrigiano, G.G., and Marder, E. (1993). Frequency and burst duration in oscillating neurons and two-cell networks. Biol. Cybern., 69:375–383.PubMedGoogle Scholar
  46. Taga, G. (1995). A model of the neuro-musculo-skeletal system for human locomotion: I. Emergence of basic gait. Biol. Cybern., 73:97–111.PubMedCrossRefGoogle Scholar
  47. Veltink, P.H., Franken, H.M., Van Alste, J. A., and Boom, H.B.K. (1992). Modeling the optimal control of cyclic leg movements induced by functional electrical stimulation. Int. J. Artif. Organs, 15(12):746–755.PubMedGoogle Scholar
  48. Williams T. (1992). Phase coupling in simulated chains of coupled oscillators representing the lamprey spinal cord. Neural Comput., 4:546–558.CrossRefGoogle Scholar
  49. Yamaguchi, G.T. and Zajac, F.E. (1990). Restoring unassisted natural gait to paraplegics via functional neuromuscular stimulation: a computer simulation study. IEEE Trans. BME, 37(9):886–902.CrossRefGoogle Scholar


  1. Franken, H.M., Veltink, P.H., Baardman, G., Redmeijer, R.A., and Boom, H.B.K. (1995). Cycle-to-cycle control of swing phase of paraplegic gait included by surface electrical stimulation, Med. Biol. Eng. Comput., 33:440–451.PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York, Inc. 2000

Authors and Affiliations

  • Srinath P. Jayasundera
  • James J. Abbas
  • Peter H. Veltink

There are no affiliations available

Personalised recommendations