Abstract
An important measure of the performance of a myoelectric control channel for powered artificial limbs is the myoelectric signal processor output signal-to-noise ratio (SNR). The signal and noise in this context are, respectively, the mean and variance of the estimate of some signal parameter to be used for control purposes. These quantities are determined by the signal processor, motor unit recruitment and motor unit firing statistics. The paper investigates, through analytical, simulation and experimental work, the role and significance of recruitment and firing statistics in channel performance. Equations are derived which express, for the single and multi-unit cases, channel SNR as a function of the number of active units, firing rates, action potential amplitude variation and action potential moments. A computer-simulated myoelectric signal is generated in which these variables can be controlled and SNR measured. The simulation results are compared with the theoretical and found to agree very well. Limited experiments with wire intramuscular electrodes and surface electrodes are performed to measurein vivo SNR from the biceps brachii muscle. The results of the experiments agree well with those of the simulation and theoretical work. The significance of this work is that it provides insight into the roles of important physiological parameters in myoelectric channel performance. It will also provide data necessary for the development of SNR enhancement techniques.
Similar content being viewed by others
Abbreviations
- a :
-
action potential amplitude
- E(x) :
-
expected value of variablex
- ISI :
-
interspike interval probability density function
- k :
-
moment ratio
- m :
-
number of active units
- MSV:
-
mean square value
- MUAP:
-
motor unit action potential
- p(t) :
-
action potential signal
- SNR :
-
signal-to-noise ratio
- T :
-
unit innervation time
- u(t) :
-
innervation process
- Var :
-
variance
- x(t) :
-
myoelectric signal
- y(t) :
-
squared myoelectric signal
- λ:
-
unit mean firing rate
References
Basmajian, V. J. andDeLuca, J. C. (1985)Muscles alive, 5th edn. Williams & Wilkins, 136–137.
Bendat, J. S. andPiersol, A. G. (1966)Measurement and analysis of random data. John Wiley & Sons, New York.
Brody, G., Scott, R. N. andBalasubramanian, R. (1974) Model for myoelectric signal generation.Med. & Biol. Eng.,12, 29–41.
Clamman, H. P. (1969) Statistical analysis of motor unit firing patterns in a human skeletal muscle.Biophys. J.,9, 1233–1251.
DeLuca, J. C. (1968) Myoelectric analysis of isometric contractions of the human biceps brachii. M.Sc. Thesis, University of New Brunswick, Canada.
DeLuca, J. C. (1979) Physiology and mathematics of myoelectric signals.IEEE Trans.,BME-26, 313–325.
Ferraioli, A. (1977) Signal to noise ratio of the filtered EMG in the isometric muscle contraction.Biomedizin. Technik,22, 86–92.
Filligoi, G. C. andMandarini, P. (1984) Some theoretical results on a digital EMG processor.IEEE Trans.,BME-31, 333–341.
Harba, M. andLynn, P. (1981) Optimizing the acquisition and processing of surface electromyographic signals.J. Biomed. Eng.,3, 100–106.
Hogan, N. andMann, R. W. (1980) Myoelectric signal processing: optimal estimation applied to EMG-Part II.IEEE Trans.,BME-27, 396–410.
Kreifeldt, J. G. (1971) Signal versus noise characteristics of filtered EMG used as a control source. Ibid.,BME-18, 16–22.
Lindstrom, L. andMagnusson, R. (1977) Interpretation of myoelectric power spectra: a model and its applications.Proc. IEEE,65, 653–662.
Papoulis, A. (1984)Probability, random variables, and stochastic processes. McGraw-Hill, New York.
Parker, P. A. andScott, R. N. (1973) Statistics of the myoelectric signal from monopolar and bipolar electrodes.Med. & Biol. Eng.,11, 591–596.
Parker, P. A., Stuller, J. andScott, R. N. (1977) Signal processing for the multistate myoelectric channel.Proc. IEEE,65, 662–674.
Plonsey, R. (1969)Bioelectric phenomena. McGraw-Hill, New York.
Scott, R. N. andThompson, G. B. (1969) An improved bipolar wire electrode for electromyography.Med. & Biol. Eng.,7, 677–678.
Shiavi, R. andNeqin, M. (1975) Stochastic properties of motorneuron activity and the effect of muscular length.Biol. Cybern.,19, 231–237.
Shwedyk, E., Balasubramanian, R. andScott, R. N. (1977) A nonstationary model for the electromyogram.IEEE Trans,BME-24, 417–423.
Thusneyapan, S. andZahalak, G. I. (1989) A practical electrode-array myoprocessor for surface electromyography. Ibid.,BME-36, 295–299.
Zhang, Y. T., Parker, P. A. andScott, R. N. (1988) A multichannel model for myoelectric control. Proc. 10th. IEEE/EMBS Conf., New Orleans, USA, 1133–1134.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Zhang, Y.T., Parker, P.A. & Scott, R.N. Study of the effects of motor unit recruitment and firing statistics on the signal-to-noise ratio of a myoelectric control channel. Med. Biol. Eng. Comput. 28, 225–231 (1990). https://doi.org/10.1007/BF02442671
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF02442671