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.
authors.affiliation: Measurement and Control Engineering Research Center, Idaho State University, Pocatello, ID 83209, USA. Electronic address: Potlchan@isu.edu.
authors.forename: Chandrasekhar
authors.initials: C
authors.lastname: Potluri
authors.forename: Madhavi
authors.initials: M
authors.lastname: Anugolu
authors.forename: Marco P
authors.initials: MP
authors.lastname: Schoen
authors.forename: D
authors.initials: D
authors.lastname: Subbaram Naidu
authors.forename: Alex
authors.initials: A
authors.lastname: Urfer
authors.forename: Steve
authors.initials: S
authors.lastname: Chiu
mesh_terms: D000328:Adult; D000465:Algorithms; D004576:Electromyography / Q000379:methods*; D005260:Female; D005385:Fingers / Q000502:physiology; D005542:Forearm / Q000502:physiology; D006801:Humans; D008297:Male; D015233:Models, Statistical; D018482:Muscle, Skeletal / Q000502:physiology*; D058067:Wavelet Analysis
keywords: ADC; AIC; Akakie Information Criterion; Analog to Digital Converter; BIC; Bayesian Information Criterion; DKF; DWT; Data fusion; Daubechies; Db; Decentralized Kalman Filter; Discrete Wavelet Transform; ICA; Independent Component Analysis; KIC; Kullback Information Criterion; Muscle force estimation based on sEMG; OE; OLMFA; Optimized Linear Model Fusion Algorithm; Output Error Model; PAFA; PCA; Principle Component Analysis; Probability Analysis based Fusion Algorithm; SPFDR; Spectral Analysis Frequency Dependent Resolution; Spectral models; Surface Electromyography; VWA; Variance Weighted Average; WH; Wavelets; Weiner Hammerstein; Wiener–Hammerstein.; sEMG