Optimization of Bayesian filters and Hammerstein-Wiener models for EMG-force signals using genetic algorithm

Anish Sebastian, Parmod Kumar, Madhavi Anugolu, Marco P. Schoen, Alex Urfer, D. Subbaram Naidu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Processing electromyographic (EMG) signals for force estimation has many unknown variables that can influence the outcome or interpretation of the recorded EMG signal significantly. An array of filtering methods have been proposed over the past few years with the objective to classify motion for use in prosthetic hands. In this paper, we explore the optimal parameter settings of a set of Bayesian based EMG filters with the objective to use the filtered EMG data for system identification. System identification is utilized to establish a relationship between the measured EMG data and the generated force developed by fingers in a human hand. The proposed system identification is based on nonlinear Hammerstein-Wiener models. Optimization is also applied to find the optimal parameter settings for these nonlinear models. Genetic Algorithm (GA) is used to conduct the optimization for both, the optimal parameter settings for the Bayesian filters as well as the Hammerstein-Wiener model. The experimental results and optimization analysis indicate that the optimization can yield significant improvement in data accuracy and interpretation.

Original languageEnglish (US)
Title of host publicationProceedings of the ASME Dynamic Systems and Control Conference 2009, DSCC2009
PublisherAmerican Society of Mechanical Engineers (ASME)
Pages713-720
Number of pages8
EditionPART A
ISBN (Print)9780791848920
DOIs
StatePublished - Jan 1 2010
Event2009 ASME Dynamic Systems and Control Conference, DSCC2009 - Hollywood, CA, United States
Duration: Oct 12 2009Oct 14 2009

Publication series

NameProceedings of the ASME Dynamic Systems and Control Conference 2009, DSCC2009
NumberPART A

Other

Other2009 ASME Dynamic Systems and Control Conference, DSCC2009
CountryUnited States
CityHollywood, CA
Period10/12/0910/14/09

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    Sebastian, A., Kumar, P., Anugolu, M., Schoen, M. P., Urfer, A., & Naidu, D. S. (2010). Optimization of Bayesian filters and Hammerstein-Wiener models for EMG-force signals using genetic algorithm. In Proceedings of the ASME Dynamic Systems and Control Conference 2009, DSCC2009 (PART A ed., pp. 713-720). (Proceedings of the ASME Dynamic Systems and Control Conference 2009, DSCC2009; No. PART A). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DSCC2009-2658