A hybrid adaptive data fusion with linear and nonlinear models for skeletal muscle force estimation

Parmod Kumar; Chandrasekhar Potluri; Madhavi Anugolu; Anish Sebastian; Jim Creelman; Alex Urfer; Steve Chiu; D. Subbaram Naidu; Marco P. Schoen

doi:10.1109/CIBEC.2010.5716075

A hybrid adaptive data fusion with linear and nonlinear models for skeletal muscle force estimation

Parmod Kumar, Chandrasekhar Potluri, Madhavi Anugolu, Anish Sebastian, Jim Creelman, Alex Urfer, Steve Chiu, D. Subbaram Naidu, Marco P. Schoen

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

Position and force control are two critical aspects of prosthetic control. Surface electromyographic (sEMG) signals can be used for skeletal muscle force estimation. In this paper, skeletal muscle is considered as a system and System Identification (SI) is used to model sEMG and skeletal muscle force. The recorded sEMG signal is filtered utilizing optimized nonlinear Half-Gaussian Bayesian filter, and a Chebyshev type-II filter prepares the muscle force signal. The filter optimization is accomplished using Genetic Algorithm (GA). Multi-linear and nonlinear models are obtained with sEMG as input and skeletal muscle force of a human hand as an output. The outputs of these models are fused with a probabilistic Kullback Information Criterion (KIC) for model selection and an adaptive probability of KIC. This approach gives good estimate of the skeletal muscle force.

Original language	English (US)
Title of host publication	2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010
Pages	9-12
Number of pages	4
DOIs	https://doi.org/10.1109/CIBEC.2010.5716075
State	Published - Dec 1 2010
Event	2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010 - Cairo, Egypt Duration: Dec 16 2010 → Dec 18 2010

Publication series

Name	2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010

Other

Other	2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010
Country/Territory	Egypt
City	Cairo
Period	12/16/10 → 12/18/10

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10.1109/CIBEC.2010.5716075

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Kumar, P., Potluri, C., Anugolu, M., Sebastian, A., Creelman, J., Urfer, A., Chiu, S., Naidu, D. S., & Schoen, M. P. (2010). A hybrid adaptive data fusion with linear and nonlinear models for skeletal muscle force estimation. In 2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010 (pp. 9-12). [5716075] (2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010). https://doi.org/10.1109/CIBEC.2010.5716075

A hybrid adaptive data fusion with linear and nonlinear models for skeletal muscle force estimation. / Kumar, Parmod; Potluri, Chandrasekhar; Anugolu, Madhavi et al.

2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010. 2010. p. 9-12 5716075 (2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kumar, P, Potluri, C, Anugolu, M, Sebastian, A, Creelman, J, Urfer, A, Chiu, S, Naidu, DS & Schoen, MP 2010, A hybrid adaptive data fusion with linear and nonlinear models for skeletal muscle force estimation. in 2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010., 5716075, 2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010, pp. 9-12, 2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010, Cairo, Egypt, 12/16/10. https://doi.org/10.1109/CIBEC.2010.5716075

Kumar P, Potluri C, Anugolu M, Sebastian A, Creelman J, Urfer A et al. A hybrid adaptive data fusion with linear and nonlinear models for skeletal muscle force estimation. In 2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010. 2010. p. 9-12. 5716075. (2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010). doi: 10.1109/CIBEC.2010.5716075

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abstract = "Position and force control are two critical aspects of prosthetic control. Surface electromyographic (sEMG) signals can be used for skeletal muscle force estimation. In this paper, skeletal muscle is considered as a system and System Identification (SI) is used to model sEMG and skeletal muscle force. The recorded sEMG signal is filtered utilizing optimized nonlinear Half-Gaussian Bayesian filter, and a Chebyshev type-II filter prepares the muscle force signal. The filter optimization is accomplished using Genetic Algorithm (GA). Multi-linear and nonlinear models are obtained with sEMG as input and skeletal muscle force of a human hand as an output. The outputs of these models are fused with a probabilistic Kullback Information Criterion (KIC) for model selection and an adaptive probability of KIC. This approach gives good estimate of the skeletal muscle force.",

author = "Parmod Kumar and Chandrasekhar Potluri and Madhavi Anugolu and Anish Sebastian and Jim Creelman and Alex Urfer and Steve Chiu and Naidu, {D. Subbaram} and Schoen, {Marco P.}",

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AU - Kumar, Parmod

AU - Potluri, Chandrasekhar

AU - Anugolu, Madhavi

AU - Sebastian, Anish

AU - Creelman, Jim

AU - Urfer, Alex

AU - Chiu, Steve

AU - Naidu, D. Subbaram

AU - Schoen, Marco P.

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N2 - Position and force control are two critical aspects of prosthetic control. Surface electromyographic (sEMG) signals can be used for skeletal muscle force estimation. In this paper, skeletal muscle is considered as a system and System Identification (SI) is used to model sEMG and skeletal muscle force. The recorded sEMG signal is filtered utilizing optimized nonlinear Half-Gaussian Bayesian filter, and a Chebyshev type-II filter prepares the muscle force signal. The filter optimization is accomplished using Genetic Algorithm (GA). Multi-linear and nonlinear models are obtained with sEMG as input and skeletal muscle force of a human hand as an output. The outputs of these models are fused with a probabilistic Kullback Information Criterion (KIC) for model selection and an adaptive probability of KIC. This approach gives good estimate of the skeletal muscle force.

AB - Position and force control are two critical aspects of prosthetic control. Surface electromyographic (sEMG) signals can be used for skeletal muscle force estimation. In this paper, skeletal muscle is considered as a system and System Identification (SI) is used to model sEMG and skeletal muscle force. The recorded sEMG signal is filtered utilizing optimized nonlinear Half-Gaussian Bayesian filter, and a Chebyshev type-II filter prepares the muscle force signal. The filter optimization is accomplished using Genetic Algorithm (GA). Multi-linear and nonlinear models are obtained with sEMG as input and skeletal muscle force of a human hand as an output. The outputs of these models are fused with a probabilistic Kullback Information Criterion (KIC) for model selection and an adaptive probability of KIC. This approach gives good estimate of the skeletal muscle force.

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A hybrid adaptive data fusion with linear and nonlinear models for skeletal muscle force estimation

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