Comput. Biol. Med. - Hybrid fusion of linear, non-linear and spectral models for the dynamic modeling of sEMG and skeletal muscle force: an application to upper extremity amputation.

Tópicos

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Resumo

Estimating skeletal muscle (finger) forces using surface Electromyography (sEMG) signals poses many challenges. In general, the sEMG measurements are based on single sensor data. In this paper, two novel hybrid fusion techniques for estimating the skeletal muscle force from the sEMG array sensors are proposed. The sEMG signals are pre-processed using five different filters: Butterworth, Chebychev Type II, Exponential, Half-Gaussian and Wavelet transforms. Dynamic models are extracted from the acquired data using Nonlinear Wiener Hammerstein (NLWH) models and Spectral Analysis Frequency Dependent Resolution (SPAFDR) models based system identification techniques. A detailed comparison is provided for the proposed filters and models using 18 healthy subjects. Wavelet transforms give higher mean correlation of 72.6 ? 1.7 (mean ? SD) and 70.4 ? 1.5 (mean ? SD) for NLWH and SPAFDR models, respectively, when compared to the other filters used in this work. Experimental verification of the fusion based hybrid models with wavelet transform shows a 96% mean correlation and 3.9% mean relative error with a standard deviation of ? 1.3 and ? 0.9 respectively between the overall hybrid fusion algorithm estimated and the actual force for 18 test subjects' k-fold cross validation data.

Resumo Limpo

estim skelet muscl finger forc use surfac electromyographi semg signal pose mani challeng general semg measur base singl sensor data paper two novel hybrid fusion techniqu estim skelet muscl forc semg array sensor propos semg signal preprocess use five differ filter butterworth chebychev type ii exponenti halfgaussian wavelet transform dynam model extract acquir data use nonlinear wiener hammerstein nlwh model spectral analysi frequenc depend resolut spafdr model base system identif techniqu detail comparison provid propos filter model use healthi subject wavelet transform give higher mean correl mean sd mean sd nlwh spafdr model respect compar filter use work experiment verif fusion base hybrid model wavelet transform show mean correl mean relat error standard deviat respect overal hybrid fusion algorithm estim actual forc test subject kfold cross valid data

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