Online estimation of EMG signals model based on a renewal process

Author

Monsifrot, J. ; Le Carpentier, Eric ; Aoustin, Y. ; Farina, Dario

Author_Institution

Inst. de Rech. en Commun. et Cybernetique de Nantes, LUNAM Univ., Nantes, France

fYear

2013

Firstpage

944

Lastpage

948

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.

Keywords

Bayes methods; Weibull distribution; biomedical electrodes; electromyography; hidden Markov models; medical signal processing; parameter estimation; Bayes filter; EMG signals model; discrete Weibull distribution; estimation complexity reduction; hidden Markov model; iEMG signals; intramuscular electromyographic signal; multiinput renewal Markov process; muscular activity; noise; online parameter estimation; physiological generation; renewal process; sparse spikes trains; wire electrodes; Computational modeling; Electromyography; Estimation; Hidden Markov models; Markov processes; Muscles; Shape; Markov process; Weibull distribution; bayesian method; parameter estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on

Conference_Location

Vancouver, BC

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2013.6637788

Filename

6637788