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Online estimation of EMG signals model based on a renewal process

Abstract : The paper presents an online estimation of parameters of a multi-input renewal Markov process. The underlying model is derived from the physiological generation of intramuscular electromyographic (iEMG) signals, which are recorded by wire electrodes. The iEMG is the sum of several sparse spikes trains and noise. An hidden Markov model, whose parameters express the muscular activity, is developed. The time duration between spikes is modeled with a discrete Weibull distribution, helping us to reduce the complexity of the estimation done with the help of a Bayes filter.
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Contributor : Jonathan Monsifrot <>
Submitted on : Friday, April 12, 2013 - 2:36:33 PM
Last modification on : Tuesday, September 21, 2021 - 4:12:07 PM


  • HAL Id : hal-00812591, version 1


Jonathan Monsifrot, Éric Le Carpentier, Yannick Aoustin, Dario Farina. Online estimation of EMG signals model based on a renewal process. IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2013, Vancouver, Canada. ⟨hal-00812591⟩



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