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 journalArticlepeer-review

11 Scopus citations


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
Issue number11
StatePublished - Nov 2013
Externally publishedYes

Bibliographical note

Funding Information:
Madhavi Anugolu received her B.E. in Electronics and Instrumentation Engineering from Acharya Nagarjuna University, Andhra Pradesh, India in 2004, M.S. in Measurement and Control Engineering from Idaho State University (ISU), in 2010. She is currently a Ph.D. candidate in Engineering and Applied Science at ISU. She worked as Graduate Research Assistant at Measurement and Control Engineering Research Center (MCERC) in the Smart Prosthetic Hand Development Project funded by the US Department of Defense from 2007 to 2012. She has 14 publications till date. She is working as a Graduate Teaching Assistant for the School of Engineering at ISU. She is also externaly serving as a reviewer for several journals and international conferences. She is the recipient of Associated Students of Idaho State University Scholarship in 2011. Her research interests include Uncertainty Analysis, Data Fusion, Smart Prostheses, Artificial Intelligence and Machine Learning.

Funding Information:
This research was sponsored by the U.S. Department of the Army , under the Award number w81xwh-10-1-0128 awarded and administered by the U.S. Army Medical Research Acquisition Activity, 820 Chandler Street, Fort Detrick, MD 21702-5014. The information does not necessarily reflect the position or the policy of the government, and no official endorsement should be inferred. For the purposes of this article, information includes news releases, articles, manuscripts, brochures, advertisements, still and motion pictures, speeches, trade association proceedings, etc. Further, the help from Dr. Haydie Lecorbeiller in proof reading the manuscript is greatly appreciated. Finally the authors appreciate very much the detailed comments from the reviewers which enhanced the quality of the manuscript.


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


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