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.
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Acknowledgment
The authors would like to thank the Posts and Telecommunications Institute of Technology, Ho Chi Minh City Branch for funding this research in project 03-HV-2017-RD-CB2.
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Luu, G.T., Boualem, A., Ravier, P., Buttelli, O. (2018). An Effective Time Varying Delay Estimator Applied to Surface Electromyographic Signals. In: Cong Vinh, P., Ha Huy Cuong, N., Vassev, E. (eds) Context-Aware Systems and Applications, and Nature of Computation and Communication. ICTCC ICCASA 2017 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-77818-1_21
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