Abstract
A review of some recent progress in improving the fidelity of electromyogram-based estimates of muscle mechanical activity is presented. Initially, theoretical techniques and experimental investigations into electromyogram amplitude estimation are described. A stochastic functional model of the surface electromyogram, which incorporates single channel optimization (temporal whitening) and spatial combination of multiple channels, is described. Compared to standard electromyogram amplitude estimators, experimental investigations confirm that the model-based estimators improve the amplitude estimate by as much as a factor of 3–4. These experimental investigations also suggest the need for a new functional electromyogram model which includes an additive noise source. Sensitivities of the temporal whitening and spatial combination algorithms are reported.
Because muscles are not commonly used in isolation, it is necessary to model both agonist and antagonist muscles about a joint in order to relate the electromyogram to joint torque and stiffness. A simple model of the elbow joint, which incorporates co-contraction, is reviewed. The model predicts that co-contraction often leads to antagonist muscle activation levels that are significantly larger than the corresponding level of agonist activation, that is, muscles supporting the limb against gravity will not work as hard as their antagonist counterparts in the presence of significant co-contraction. Experimental observations confirm this prediction. This same agonist/antagonist muscle model then is used to relate the surface electromyogram to joint torque. Simulation studies that investigated the co-contraction model demonstrate that robust joint torque estimation could be accomplished in the presence of muscular co-contraction. In an experimental trial, the higher fidelity electromyogram amplitude estimators produced higher fidelity electromyogram-to-torque processors.
Portions reprinted with permission from; (5,6,7,8,20,21,22), ©IEEE 1990, 1991, 1994, 1995, 1988, 1988, 1988, respectively, and; (9), ©Liberty Mutual Insurance Company, 1995.
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
Preview
Unable to display preview. Download preview PDF.
References
Amis, A. A., Dowson, D., and Wright, V., (1979), Muscle strengths and musculoskeletal geometry of the upper limb, Engng. Med. 8 (1): 41–8.
Basmajian, J. V., and DeLuca, C. J. (1985), Muscles Aline: Their Functions Revealed by Electromyography. Baltimore, MD: Williams & Wilkins.
Brody, G., Scott, R. N., and Balasubramanian, R. A (1974), a model for myoelectric signal generation, Med. Biol. Eng. Jan: 29–41.
Clancy, E. A. (1991), Stochastic modeling of the relationship between the surface electromyogram and muscle torque, Ph.D. dissertation, Dept. Elect. Eng. Comp. Sci., M. I. T., Cambridge, MA.
Clancy. E. A., and Hogan, N. (1990), EMG amplitude estimation from temporally whitened, spatially uncorrelated multiple channel EMG, Ann. Int. Conf IEEE Eng. Med. Bio. Soc. 12: 0453–0454.
Clancy, E. A., Hogan, N. (1991), Estimation of joint torque from the surface EMG, Ann. Int. Conf. IEEE Eng. Med. Bio. Soc. 13: 0877–0878.
Clancy, E.A., and Hogan, N. (1994), Single site electromyograph amplitude estimation. IEEE Trans. Biomed. Eng. 41: 159–167.
Clancy, E.A., and Hogan, N. (1995), Multiple site electromyograph amplitude estimation. IEEE Trans. Biomed. Eng. 42: 203–211.
Clancy, E. A., Murray, W. R., and Hogan, N. (1995), A review of some recent progress in processing the surface myoelectric signal. Technical Memorandum 95–6 (unpublished), Liberty Mutual Research Center for Safety and Health, Hopkinton, MA.
DeLuca, C. J. (1979), Physiology and mathematics of myoelectric signals. IEEE Trans. Biomed. Eng. 26: 313–25.
DeLuca, C. J., and Van Dyk, E. J. (1975), Derivation of some parameters of myoelectric signals recorded during sustained constant force isometric contractions, Bioplrvs. J. 15: 1167–1180.
Hogan, N. (1984), Adaptive control of mechanical impedance by coactivation of antagonist muscles. IEEE Trans. Automat. Cont. 16: 681–690.
Hogan, N., and Mann, R. W. (1980), Myoelectric signal processing: Optimal estimation applied to electromyography Part I: Derivation of the optimal myoprocessor, IEEE Trans. Biomed. Eng. 27: 38–295.
Hogan, N., and Mann, R. W. (1980), Myoelectric signal processing: Optimal estimation applied to electromyography-Part II: Experimental demonstration of optimal myoprocessor performance, IEEE Trans. Biomed. Eng. 27: 396–410.
Joyce, G. C., Rack, P. M. H., and Westbury, D. R. (1969), The mechanical properties of cat soleus muscle during controlled lengthening and shortening movements, J. Physiol. 204: 461–474.
Kaiser, E., and Petersen, I. (1974), Adaptive filter for EMG control signals. In: The Control of Upper-Extremity Prostheses and Orthoses. Springfield, IL: Charles C. Thomas. pp. 54–57.
Kwatny, E., Thomas, D. H., and Kwatny, H. G. (1970), An application of signal processing techniques to the study of myoelectric signals, IEEE Trans. Biomed. Eng. 17: 303–313.
Morgan, D. L. (1977), Separation of active and passive components of short-range stiffness of muscle. Am. J. Physiol. 232 (1): C45 - C49.
Murray, W. R. (1988), Essential factors in modeling the modulation of impedance about the human elbow. Ph.D. dissertation, Dept. Mech. Eng., M. I. T., Cambridge, MA.
Murray, W. R. (1988), Maintenance of elbow equilibrium through co-contraction. Proc. Fourteenth Ann. Northeast Bioeng. Conf. pp. 29–32.
Murray, W. R. (1988). Modeling elbow equilibrium in the presence of co-contraction. Proc. Fourteenth Ann. Northeast Bioeng. Conf pp. 190–193.
Murray, W. R., and Hogan, N. (1988), Co-contraction of antagonist muscles: Predictions and observations. Ann. Int. Conf. IEEE Eng. Med. Biol. Soc. 10: 1926–1927.
Parker, P. A., Stutter. J. A, and Scott, R. N. (1977), Signal processing for the multistate myoelectric channel. Proc. IEEE 65 (5): 662–674.
Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T. (1988), Numerical Recipes in C: The Art of Scientific Computing. New York: Cambridge University Press, pp. 528–539.
Rack, P. M. H., and Westbury, D.R. (1969), The effects of length and stimulons rate on tension in the isometric cat soleus muscle. J. Phys. 204: 443–460.
Roesler, H. (1974), Statistical analysis and evaluation of myoelectric signals for proportional control. In: The Control of Upper-Extremity Prostheses and Orthoses. Springfield, IL: Charles C. Thomas, pp. 44–53.
Shwedyk, E., Balasubramanian, R., and Scott, R. N. (1977), A nonstationary model for the electromyogram, IEEE Trans. Biomed. Eng. 24: 417–24.
Wilkie, D. R. (1950), The relationship between force and velocity in human muscles, J. Physiol. 110: 249–280.
Zahalak, G. I., Heyman. S. J. (1979). A quantitative evaluation of the frequency-response characteristics of active human skeletal muscle in vivo, J. Biomech. Eng. 101: 28–37.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1996 Springer Science+Business Media New York
About this chapter
Cite this chapter
Clancy, E.A., Murray, W.R., Hogan, N. (1996). Multi-Channel EMG Processing. In: Gath, I., Inbar, G.F. (eds) Advances in Processing and Pattern Analysis of Biological Signals. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9098-6_24
Download citation
DOI: https://doi.org/10.1007/978-1-4757-9098-6_24
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-9100-6
Online ISBN: 978-1-4757-9098-6
eBook Packages: Springer Book Archive