Name
Title | ||
|---|---|---|
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. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.compbiomed.2013.08.023 | Comp. in Bio. and Med. |
Keywords | DocType | Volume |
mean correlation,Weiner Hammerstein,novel hybrid fusion technique,Independent Component Analysis,Akakie Information Criterion,Bayesian Information Criterion,Principle Component Analysis,Wavelets,semg signal,Wiener–Hammerstein.,VWA,semg measurement,BIC,WH,semg array sensor,Surface Electromyography,DWT,Optimized Linear Model Fusion Algorithm,Spectral Analysis Frequency Dependent Resolution,PAFA,single sensor data,Discrete Wavelet Transform,sEMG,Daubechies,skeletal muscle force,Analog to Digital Converter,Muscle force estimation based on sEMG,Decentralized Kalman Filter,DKF,ADC,Probability Analysis based Fusion Algorithm,acquired data,Kullback Information Criterion,higher mean correlation,Output Error Model,PCA,spectral model,OE,OLMFA,Variance Weighted Average,hybrid model,upper extremity amputation,AIC,dynamic modeling,Spectral models,SPFDR,ICA,overall hybrid fusion algorithm,Data fusion,KIC,Db | Journal | 43 |
Issue | ISSN | Citations |
11 | 1879-0534 | 0 |
PageRank | References | Authors |
0.34 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chandrasekhar Potluri | 1 | 5 | 3.17 |
| Madhavi Anugolu | 2 | 2 | 1.81 |
| Marco P Schoen | 3 | 10 | 4.54 |
| D. Subbaram Naidu | 4 | 17 | 10.06 |
| Alex Urfer | 5 | 0 | 1.35 |
| Steve Chiu | 6 | 0 | 1.01 |