Abstract
This paper deals with the online decomposition of intramuscular electromyographic (iEMG) signals. A Markov model is proposed, which takes into account a varying number of firing motor neurons. A Bayes filter detects online the firing motor units by using a dictionary of approximated motor unit action potentials waveforms, and estimates precisely the action potential shapes and the respective firing rates. The method was tested on both simulated and experimental signals.
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References
Wiener, N.: CYBERNETICS or control and communication in he animal and the machine. MIT Press (1948)
Castellini, C., van der Smagt, P., Sandini, G., Hirzinger, G.: Surface EMG for force control of mechanical hands. In: IEEE Int. Conf. on Robotics and Automation 2008, pp. 725–730 (May 2008)
Akazawa, K., Takizawa, H., Hayashi, K., Fujii, K.: Development of control system and myoelectric signal processor for bio-mimetic prosthetic hand. Biomechanism 9 19(4), 43–53 (1988)
Tsukamoto, M., Kondo, T., Ito, K.: A prosthetic hand control based on non stationary emg at the start of movement. J. of Robotics and Mechatronics 19(4), 382–387 (2007)
Abul-haj, C.J., Hogan, N.: Functional assessment of control systems for cybernetic elbow prostheses-part i, part ii. IEEE Trans. Biomed. Engineering 37(11), 1025–1047 (1990)
Englehart, K., Hudgins, B., Parker, P., Stevenson, M.: Classification of the myoelectric signal using time-frequency based representations. Medical Engineering and Physics 21, 431–438 (1999)
Zecca, M., Micera, S., Carrozza, M.C., Dario, P.: Control of multifunctional prosthetic hands by processing the electromyographic signal. Critical Reviews in Biomedical Engineering 30(4-6), 459–485 (2002)
Kamavuako, E.N., Englehart, K.B., Jensen, W., Farina, D.: Simultaneous and Proportional Force Estimation in Multiple Degrees of Freedom From Intramuscular EMG. IEEE Trans. Biomed. Engineering 59(7), 1804–1807 (2012)
Mambrito, B., De Luca, C.: A Technique for the Detection, Decomposition and Analysis of the EMG Signal. Electroencephalography and Clinical Neurophysiology 58, 175–188 (1984)
Lefever, R., De Luca, C.: A Procedure for Decomposing the Myoelectric Signal Into Its Constituent Action Potentials - Part I: Technique, Theory, and Implementation. IEEE Trans. Biomed. Engineering 29(3), 149–157 (1982)
Lefever, R., De Luca, C.: A Procedure for Decomposing the Myoelectric Signal Into Its Constituent Action Potentials - Part II: Execution and Test for Accuracy. IEEE Trans. Biomed. Engineering 29(3), 158–164 (1982)
Marateb, H., Muceli, S., Mcgill, K., Merletti, R., Farina, D.: Robust decomposition of single-channel intramuscular EMG signals at low force levels. IEEE Trans. on Automatic Control 8(6), 1–13 (2011)
Ge, D., Le Carpentier, E., Farina, D.: Unsupervised Bayesian Decomposition of Multiunit EMG Recordings Using Tabu Search. IEEE Trans. Biomed. Engineering 57(3), 561–570 (2010)
Monsifrot, J., Le Carpentier, E., Aoustin, Y., Farina, D.: Sequential Decoding of Intramuscular EMG Signals via Estimation of a Markov Model. IEEE Transactions on Neural Systems and Rehabilitation Engineering (to appear, 2014)
Nakagawa, T., Osaki, S.: The discrete Weibull distribution. IEEE Trans. on Reliability R-24(5), 300–301 (1975)
Stashuk, D.: EMG signal decomposition: how can it be accomplished and used? J. of Electromyography and Kinesiology 11(3), 151–173 (2001)
Farina, D., Crosetti, A., Merletti, R.: A model for the generation of synthetic intramuscular EMG signals to test decomposition algorithms. IEEE Trans. Biomed. Engineering 48(1), 66–77 (2001)
Doucet, A., De Freitas, N., Gordon, N.: Sequential Monte Carlo methods in practice. Springer (2001)
Ljung, L., Söderström, T.: Theory and Practice of Recursive Identification. The MIT Press, Massachusetts (1983)
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Le Carpentier, E., Aoustin, Y., Monsifrot, J., Farina, D. (2014). Online Intramuscular EMG Decomposition with Varying Number of Active Motor Units. In: Jensen, W., Andersen, O., Akay, M. (eds) Replace, Repair, Restore, Relieve – Bridging Clinical and Engineering Solutions in Neurorehabilitation. Biosystems & Biorobotics, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-08072-7_50
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DOI: https://doi.org/10.1007/978-3-319-08072-7_50
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08071-0
Online ISBN: 978-3-319-08072-7
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