The Electromyogram (EMG) as a Control Signal for Functional Neuromuscular Stimulation—Part I: Autoregressive Modeling as a Means of EMG Signature Discrimination

IEEE Transactions on Biomedical Engineering

Volumn 35, Issue 4, 1988, Pages 230-237

  Hefftner, Gisela ^a   Zucchini, Walter ^a   Jaros, George G ^a  

^a UNIVERSITY OF CAPE TOWN   (South Africa)

Author keywords

[No Author keywords available]

Indexed keywords

ORTHOTICS - FUNCTIONAL NEURAL STIMULATION; SIGNAL PROCESSING - SPECTRUM ANALYSIS;

AUTOREGRESSIVE MODELING; ELECTROMYOGRAM (EMG); EMG SIGNATURE DISCRIMINATION; FUNCTIONAL NEUROMUSCULAR STIMULATION;

BIOMEDICAL ENGINEERING;

ELECTROMYOGRAM; METHODOLOGY; MODEL; NEUROMUSCULAR SYSTEM; PARAPLEGIA; STIMULATION; THEORETICAL STUDY;

ELECTRIC STIMULATION; ELECTROMYOGRAPHY; EXTREMITIES; HUMAN; MODELS, THEORETICAL; MOVEMENT; MUSCLES; PARALYSIS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 0024000563     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/10.1370     Document Type: Article

References (27)

1. L. Vodovnik, T. Bajd, and A. Kralj, “Functional electrical stimulation for control of motor systems, ” in CRC Critical Reviews in Bioengineering, 1981. pp. 63–131.
2. J. S. Petrofsky, C.A. Phillips, and H. H. Heaton, “Feedback control system for walking in man,” Comput. Biol. Med., vol. 14, no. 2, pp. 135–149, 1984.
3. G. R. Cybulski, R. D. Penn, and R. J. Jaeger, “Lower extremity neuromuscular stimulation in cases of spinal cord injury,” Neurosurg., vol. 15, pp. 132–146, 1984.
4. D. Graupe, K. H. Kohn, A. Kralj, and S. Basseas, “Patient controlled electrical stimulation via EMG signature discrimination for providing certain paraplegics with primitive walking functions,” J. Biomed. Eng., vol. 5, pp. 220–226, 1983.
5. H. B. Evans, Z. Pan, A. P. Philip, and R. N. Scott, “Signal processing for proportional myoelectric control,” IEEE Trans. Biomed. Eng., vol. BME-31, pp. 207–211, Feb. 1984.
6. S. M. Fleisher and E. Shwedyk, “Sequential multistate EMG signal processor,” IEEE Trans. Biomed. Eng., vol. BME-26, pp. 549–556, Oct. 1979.
7. J. L. Berube, P. A. Parker, R. E. Gander, and V. A. Dunfield, “Digital myoelectric signal processor with adaptive decision boundaries,” Med. Biol. Eng. Comput., vol. 22, no. 4, pp. 349–352, 1984.
8. L. Philipson, “Adaptable myoelectric prosthetic control with functional visual feedback using microprocessor techniques,” Med. Biol. Eng. Comput., vol! 23, pp. 8–14, 1985.
9. N. Hogan, “A review of the methods of processing EMG for use as a proportional control signal,” Biomed. Eng., vol. 11, pp. 81–86, 1976.
10. P. H. Peckham, E. B. Marsolais, and J. T. Mortimer, “Restoration of key grip and release in the C6 tetraplegie patient through functional electrical stimulation,” J. Hand Surg., vol. 5, no. 5, pp. 462–469, 1980.
11. G. N. Saridis and T. P. Gootee, “EMG pattern analysis and classification for a prosthetic arm,” IEEE Trans. Biomed. Eng., vol. BME-29, pp. 403–412, June 1982.
12. M. H. Sherif, R. J. Gregor, and J. Lyman, “Effects of load on myoelectric signals: The ARIMA representation,” IEEE Trans. Biomed. Eng., vol. BME-28, pp. 411–416, May 1981.
13. P. C. Doerschuk, D. E. Gustafson, and A. S. Willsky, “Upper extremity limb function discrimination using EMG signal analysis,” IEEE Trans. Biomed. Eng., vol. BME-30, pp. 18–28, Jan. 1983.
14. R. J. Triolo and G. D. Moskowitz, “A multichannel time series myoprocessor for robust classification of limb function and estimation of muscle force,” in IEEE Proc. 7th Annu. Conf. Eng. Med. Biol. Soc, 1985, pp. 265–270.
15. P. B. C. Fenwick, P. Michie, J. Dollimore, and G. W. Fenton, “Mathematical simulation of the electroencephalogram using an autoregressive series,” Bio-Med. Comput., vol. 2, pp. 281–307, 1971.
16. G. J. De Luca, “Physiology and mathematics of myoelectric signals,” IEEE Trans. Biomed. Eng., vol. BME-26, pp. 313–325, June 1979.
17. N. Hogan and R. W. Mann, “Myoelectric signal processing: Optimal estimation applied to electromyography–Part II: Experimental demonstration of optimal myoprocessor performance,” IEEE Trans. Biomed. Eng., vol. BME-27, pp. 396–410, July 1980.
18. M. H. Sherif, R. J. Gregory, and J. Lyman, “Comments on ‘Upper extremity limb function discrimination using EMG signal analysis,” IEEE Trans. Biomed. Eng., vol. BME-31, p. 440, May 1984.
19. J. Makhoul, “Linear prediction: A tutorial review,” Proc. IEEE, vol. 63, pp. 561–580, Apr. 1975.
20. D. Graupe, V. K. Jain, and J. Salahi, “A comparative analysis of various least-squares identification algorithms,” Automatica, vol. 16, pp. 663–681, 1980.
21. D. Graupe and W. K. Cline, “Functional separation of EMG signals via ARMA identification methods for prosthesis control purposes,” IEEE Trans. Syst., Man, Cybern., vol. SMC-5, no. 2, pp. 252–259, 1975.
22. D. Graupe, Identification of Systems, 2nd ed. Malabar: Krieger, 1976, sect. 5, 6, 12.6.
23. G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control, 2nd ed. San Francisco: Holden-Day, 1976.
24. J. S. Bendat and A. G. Piersol, Random Data: Analysis and Measurement Procedures. New York: Wiley, 1971.
25. G. Hefftner, “The electromyogram as a control signal for functional electrical stimulation,” M.Sc. thesis, Dep. Biomed. Eng. Univ. Cape Town, South Africa, 1986.
26. E. Kwatny, D. H. Thomas, and H. G. Kwàtny, “An application of signal processing techniques to the study of myoelectric signals,” IEEE Trans. Biomed. Eng., vol. BME-17, pp. 303–312, Apr. 1970.
27. D. Graupe, J. Salahi, and Z. Desong, “Stochastic analysis of myoelectric temporal signatures for multifunctional single-site activation of prostheses and orthoses,” J. Biomed. Eng., vol. 7, pp. 18–29, 1985.