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

Chandrasekhar Potluri, Madhavi Anugolu, Marco P. Schoen, D. Subbaram Naidu, Alex Urfer, Steve Chiu

Research output: Contribution to journalArticle

8 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1815-1826
Number of pages12
JournalComputers in Biology and Medicine
Volume43
Issue number11
DOIs
StatePublished - Nov 1 2013
Externally publishedYes

Keywords

  • Data fusion
  • Muscle force estimation based on sEMG
  • Spectral models
  • Wavelets
  • Wiener-Hammerstein.

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