The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS
Transactions ID Number: 88-387
Full Name: Marco Schoen
Position: Professor
Age: ON
Sex: Male
Address: 921 South 8th Ave., Stop 8060, Pocatello, Idaho
Country: UNITED STATES
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E-mail address: schomarc@isu.edu
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Title of the Paper: Adaptive Multi Sensor Based Nonlinear Identification of Skeletal Muscle Force
Authors as they appear in the Paper: Parmod Kumar, Chandrasekhar Potluri, Anish Sebastian, Steve Chiu, Alex Urfer, D. Subbaram Naidu, Marco P. Schoen
Email addresses of all the authors: kumaparm@isu.edu,chandupotluri@gmail.com,sebaanis@isu.edu,chiustev@gmail.com,urfealex@isu.edu,naiduds@isu.edu,schomarc@isu.edu
Number of paper pages: 13
Abstract: Skeletal muscle force and surface electromyographic (sEMG) signals are closely related. Hence, the later can be used for the force estimation. Usually, the location for the sEMG sensors is near the respective muscle motor unit points. EMG signals generated by skeletal muscles are temporal and spatially distributed which results in cross talk that is recorded by different sEMG sensors. This research focuses on modeling muscle dynamics in terms of sEMG signals and the generated muscle force. Here, an array of three sEMG sensors is used to capture the information of the muscle dynamics in terms of sEMG signals and generated muscle force. Optimized nonlinear Half-Gaussian Bayesian filters and a Chebyshev type-II filter are used for the filtration of the sEMG signals and the muscle force signal, respectively. A Genetic Algorithm is used for the optimization of the filter parameters. sEMG and skeletal muscle force is modeled using multi nonlinear Auto Regressive eXogenou!
s (ARX) and Wiener-Hammerstein models with different nonlinearity estimators/classes using System Identification (SI) for three sets of sensor data. An adaptive probabilistic Kullback Information Criterion (KIC) for model selection is applied to obtain the fusion based skeletal muscle force for each sensor first and then for the final outputs from each sensor. The approach yields good skeletal muscle force estimates.
Keywords: sEMG, ARX, Weiner-Hammerstein, Prosthetic hand, KIC, System Identification
EXTENSION of the file: .pdf
Special (Invited) Session: An Adaptive Multi Sensor Data Fusion with Hybrid Nonlinear ARX and Wiener-Hammerstein Models for Skeletal Muscle Force Estimation
Organizer of the Session: 646-281
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