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.
ASME 2009 Dynamic Systems and Control Conference
October 12–14, 2009
Hollywood, California, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-4892-0
PROCEEDINGS PAPER
Optimization of Bayesian Filters and Hammerstein-Wiener Models for EMG-Force Signals Using Genetic Algorithm
Anish Sebastian
,
Anish Sebastian
Idaho State University, Pocatello, ID
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Parmod Kumar
,
Parmod Kumar
Idaho State University, Pocatello, ID
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Madhavi Anugolu
,
Madhavi Anugolu
Idaho State University, Pocatello, ID
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Marco P. Schoen
,
Marco P. Schoen
Idaho State University, Pocatello, ID
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Alex Urfer
,
Alex Urfer
Idaho State University, Pocatello, ID
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D. Subbaram Naidu
D. Subbaram Naidu
Idaho State University, Pocatello, ID
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Author Information
Anish Sebastian
Idaho State University, Pocatello, ID
Parmod Kumar
Idaho State University, Pocatello, ID
Madhavi Anugolu
Idaho State University, Pocatello, ID
Marco P. Schoen
Idaho State University, Pocatello, ID
Alex Urfer
Idaho State University, Pocatello, ID
D. Subbaram Naidu
Idaho State University, Pocatello, ID
Paper No:
DSCC2009-2658, pp. 713-720; 8 pages
Published Online:
September 16, 2010
Citation
Sebastian, Anish, Kumar, Parmod, Anugolu, Madhavi, Schoen, Marco P., Urfer, Alex, and Naidu, D. Subbaram. "Optimization of Bayesian Filters and Hammerstein-Wiener Models for EMG-Force Signals Using Genetic Algorithm." Proceedings of the ASME 2009 Dynamic Systems and Control Conference. ASME 2009 Dynamic Systems and Control Conference, Volume 1. Hollywood, California, USA. October 12–14, 2009. pp. 713-720. ASME. https://doi.org/10.1115/DSCC2009-2658
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