Journal of Medical Systems

, Volume 34, Issue 4, pp 435–443 | Cite as

Fuzzy Approach for Determination the Optimum Therapeutic Parameters in Neuromuscular Stimulation Systems

  • Mashhour M. Bani Amer
  • Lina Al-Ebbini
Original Paper


This paper presents a fuzzy system for determination the optimum therapeutic parameters (frequency, amplitude and session duration) in neuromuscular stimulation systems. The developed approach has many clinical benefits and features. These include: capability to determine and adjust the therapeutic parameters online during any treatment session, reduction of time and number of sessions needed to overcome the neuromuscular disorders, preventing patient from fatigue or pain that may occur during or after treatment, considering differences between various patients and improving the efficiency of the available neuromuscular stimulation systems.


Fuzzy approach Therapeutic parameters Neuromuscular stimulation 


  1. 1.
    Kordylewski, H., and Grampe, D., Control of neuromuscular stimulation for ambulation by complete paraplegia via artificial neural networks. Neurol. Res. 23:472–481, 2001. doi: 10.1179/016164101101198866.CrossRefGoogle Scholar
  2. 2.
    Salmon, S., The application and technology of implantable neuromuscular stimulators. Med. Eng. Phys. 23:1–3, 2001. doi: 10.1016/S1350-4533(01)00004-2.CrossRefGoogle Scholar
  3. 3.
    Burridge, J. H., and Ladonceur, M., Clinical and therapeutic applications of neuromuscular stimulation, a review of current use and speculation into future developments. J. Int. Neuromodulation Soc. 4(4)147–154, 1999.CrossRefGoogle Scholar
  4. 4.
    Jorgovanović, N., Strahinja, D., and Ratko, P., Novel electronic stimulator for functional electrical therapy. Journal Of Automatic Control. 15:27–30, 2005.Google Scholar
  5. 5.
    Kilgore, K. L., Peckman, P. H., et al., Synthesis of hand grasp using functional neuromuscular stimulation. IEEE Trans. Biomed. Eng. 36(7)761–770, 1989. doi: 10.1109/10.32109.CrossRefGoogle Scholar
  6. 6.
    Cargo, P. E., Nakai, R. J., and Chizeck, H. J., Feedback regulation of hand grasp opening and contact force during stimulation of paralyzed muscle. IEEE Trans. Biomed. Eng. 38(1)17–29, 1991. doi: 10.1109/10.68205.CrossRefGoogle Scholar
  7. 7.
    Schmitt, C., Métrailler, P., et al., A study of a knee extension controlled by a closed loop functional electrical stimulation. Annual Conference of the International FES Society, September, 2004.Google Scholar
  8. 8.
    Qi, H., Tyler, D. J., and Durand, D. M., Neurofuzzy adaptive controlling of selective stimulation for FES: a case study. IEEE Trans. Rehabil. Eng. 7(2)183–192, 1999. doi: 10.1109/86.769409.CrossRefGoogle Scholar
  9. 9.
    Davoodi, R., and Andrews, B. J., Computer simulation of FES standing up in paraplegia: a self-adaptive fuzzy controller with reinforcement learning. IEEE Trans. Rehabil. Eng. 6(2)151–161, 1998. doi: 10.1109/86.681180.CrossRefGoogle Scholar
  10. 10.
    The Universal Interferential 94, A programmable unit with two independent tetrapolar channels, Service Manual, Carin Company.Google Scholar
  11. 11.
    Haslett, C., Chilvers, E. R., Boon, N. A., Colledge, N. R., Principles and practice of medicine, 19th Edition, p. 298, 2002.Google Scholar
  12. 12.
    Andrew, J., Fuegland, Fuglevand, J., Macefield, V., Bigland-Ritchie, B., Force-frequency and fatigue properties of motor units in muscles that control digits of the human hand. The American Physiological Society, pp. 1718–1729, 1999.Google Scholar
  13. 13.
    Blnder-Macleod, S. A., and Snyder-Mackler, L., Muscle fatigue: clinical implications for fatigue assessment and neuromuscular electrical stimulation. Phys. Ther. 73(12)902–910, 1993.Google Scholar
  14. 14.
    Muzammil, M., Siddiqui, S. S., and Hasan, F., Physiological effect of vibrations on tractor drivers under variable ploughing conditions. J. Occup. Health. 46:3–409, 2004. doi: 10.1539/joh.46.403.CrossRefGoogle Scholar
  15. 15.
    Hunter, S. K., Critchlow, A., Enoka, R. M., Influence of aging on sex differences in muscle fatigability. The American Physiological Society,, pp. 1723–1732, 2004, June.
  16. 16.
    Mase, K., Kamimura, H., et al., Effect of age and gender on muscle function- analysis by muscle fiber conduction velocity-. J. Phys. Ther. Sci. 18:81–87, 2006. doi: 10.1589/jpts.18.81.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Biomedical Engineering, Faculty of EngineeringJordan University of Science and TechnologyIrbidJordan
  2. 2.IrbidJordan

Personalised recommendations