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

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• 作者：Ch ; rasekhar Potluri ; Madhavi Anugolu ; Marco P. Schoen ; D. Subbaram Naidu ; Alex Urfer ; Steve Chiu
• 关键词：ADC ; Analog to Digital Converter ; AIC ; Akakie Information Criterion ; BIC ; Bayesian Information Criterion ; Db ; Daubechies ; DKF ; Decentralized Kalman Filter ; DWT ; Discrete Wavelet Transform ; KIC ; Kullback Information Criterion ; OLMFA ; Optimized Linear Model Fusion Algorithm ; OE ; Output Error Model ; PAFA ; Probability Analysis based Fusion Algorithm ; PCA ; Principle Component Analysis ; ICA ; Independent Component Analysis ; sEMG ; Surface Electromyography ; SPFDR ; Spectral Analysis Frequency Dependent Re
• 刊名：Computers in Biology and Medicine
• 出版年：November, 2013
• 年：2013
• 卷：43
• 期：11
• 页码：1815-1826
• 全文大小：1172 K

文摘

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卤1.7 (mean卤SD) and 70.4卤1.5 (mean卤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 卤1.3 and 卤0.9 respectively between the overall hybrid fusion algorithm estimated and the actual force for 18 test subjects鈥?k-fold cross validation data.