Abstract
A portable close-loop control FES system, whose controller is a microcomputer, was proposed in this chapter. Considering the time-varying nonlinear of the muscle system, a self-adaptive PI control strategy was used. It included a neural network identifier (NNI) and a PI controller. NNI could get the variability of muscle working condition to identify muscle model online. Parameters of PI would be optimized by the results of NNI. Some tracking experiments had been done on able-bodied volunteers and the precision were under 4%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Rushton D.N. (2003) Functional electrical stimulation and rehabilitation—an hypothesis, Med. Eng. Phys., 25(1), 75–78
Jezernik S., Wassink R.G. and Keller T. (2004) Sliding mode closed-loop control of FES: controlling the shank movement, IEEE Trans. Biomed. Eng., 51(2), 263–272
Bobet J. and Stein R.B. (1998) A simple model of force generation by skeletal muscle during dynamic isometric contractions, IEEE Trans. Biomed. Eng., 45(8), 1010–1016
Frey Law L.A. and Shields R.K. (2006) Predicting human chronically paralyzed muscle force: a comparison of three mathematical models, J. Appl. Physiol., 100(3), 1027–1036
Rakos M., Freudenshu B., Girsch W., et al. (1999) Electromyogram-controlled functional electrical stimulation for treatment of the paralyzed upper extremity, Int. J. Artif. Organs, 23(5), 466–469
Simcox S., Davis G., Barriskill A., et al. (2004) A portable, 8-channel transcutaneous stimulator for paraplegic muscle training and mobility – A technical note. J. Rehabil. Res. Dev., 41(1), 41–52
Hart D.J., Taylor P.N., Chappell P.H., et al. (2006) A microcontroller system for investigating the catch effect: functional electrical stimulation of the common peroneal nerve, Med. Eng. Phys., 28(5), 438–448
Previdi F. and Carpanzano E. (2003) Design of a gain scheduling controller for knee-joint angle control by using functional electrical stimulation, IEEE Trans. Control Syst. Technol., 11(3), 310–324
Ferrarin M., Pavan E., Spadone R., et al. (2002) Standing-up exerciser based on functional electrical stimulation and body weight relief, Med. Biol. Eng. Comput., 40(3), 282–289
Tong K.Y. and Granat M.H. (1999) Gait control system for functional electrical stimulation using neural networks, Med. Biol. Eng. Comput., 37(1), 35–41
Kostov A., Andrews B.J., Popovic D.B., et al. (1995) Machine learning in control of functional electrical stimulation systems for locomotion, IEEE Trans. Biomed. Eng., 42(6), 541–551
Lan N., Feng H. and Crago P.E. (1994) Neural network generation of muscle stimulation patterns for control of arm movements, IEEE Trans. Rehabil. Eng., 2(4), 213–224
Sepulveda F., Granat M.H. and Cliquet A. Jr. (1998) Gait restoration in a spinal cord injured subject via neuromuscular electrical stimulation controlled by an artificial neural network, Int. J. Artif. Organs, 21(1), 49–62
Hatwell M.S., Oderkerk B.J., Sacher C.A., et al. (1991) The development of a model reference adaptive controller to control the knee joint of paraplegics, IEEE Trans. Automat. Contr., 36(6), 683–691
Bernotas L.A., Crago P.E. and Chizeck H.J. (1986) A discrete-time model of electrically stimulated muscle, IEEE Trans. Biomed. Eng., 33(9), 829–838
Rumelhart D.E., Hinton G.E. and Williams R.J. (1986) Learning representations by back-propagating errors, Nature, 323(9), 533–536
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Cao, L., Yang, JS., Geng, ZL., Cao, G. (2011). A Microcomputer FES System for Wrist Moving Control. In: Arabnia, H., Tran, QN. (eds) Software Tools and Algorithms for Biological Systems. Advances in Experimental Medicine and Biology, vol 696. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7046-6_63
Download citation
DOI: https://doi.org/10.1007/978-1-4419-7046-6_63
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-7045-9
Online ISBN: 978-1-4419-7046-6
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)