Research Article
An Effective Time Varying Delay Estimator Applied to Surface Electromyographic Signals
@INPROCEEDINGS{10.1007/978-3-319-77818-1_21, author={Gia Luu and Abdelbassit Boualem and Philippe Ravier and Olivier Buttelli}, title={An Effective Time Varying Delay Estimator Applied to Surface Electromyographic Signals}, proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 6th International Conference, ICCASA 2017, and 3rd International Conference, ICTCC 2017, Tam Ky, Vietnam, November 23-24, 2017, Proceedings}, proceedings_a={ICCASA \& ICTCC}, year={2018}, month={3}, keywords={Electromyography Time-varying delay Muscle Fiber Conduction Velocity Non-stationarity Maximum likelihood estimation Simulated annealing (SA) CRLB}, doi={10.1007/978-3-319-77818-1_21} }- Gia Luu
Abdelbassit Boualem
Philippe Ravier
Olivier Buttelli
Year: 2018
An Effective Time Varying Delay Estimator Applied to Surface Electromyographic Signals
ICCASA & ICTCC
Springer
DOI: 10.1007/978-3-319-77818-1_21
Abstract
Muscle Fiber Conduction Velocity (MFCV) can be calculated from the time delay between the surface electromyographic (sEMG) signals recorded by electrodes aligned with the fiber direction. In order to take into account the non-stationarity during the dynamic contraction (the most daily life situation) of the data, the developed methods have to consider that the MFCV changes over the time, which induces time varying delays and the data is non-stationary (change of Power Spectral Density (PSD)). In the present paper, the problem of time varying delay (TVD) estimation is considered using a parametric method. First, the polynomial model of TVD has been proposed. Then, the TVD model parameters are estimated by using a maximum likelihood estimation (MLE) strategy solved by a stochastic optimization technique, called simulated annealing (SA). The Monte-Carlo simulation results show that the estimation of both the model parameters and the TVD function is unbiased and that the variance obtained is close to the Crammer-Rao Lower Bound (CRLB). We also compared the performance of the proposed method with non-parametric approaches. The results indicate that the proposed method outperform the non-parametric one.
