Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force

Chandrasekhar Potluri; Madhavi Anugolu; Steve Chiu; Alex Urfer; Marco P. Schoen; D. Subbaram Naidu

doi:10.1109/EMBC.2012.6346620

Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force

Chandrasekhar Potluri, Madhavi Anugolu, Steve Chiu, Alex Urfer, Marco P. Schoen, D. Subbaram Naidu

Electrical Engineering

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

2 Scopus citations

Abstract

In this paper, we present a method of combining spectral models using a Kullback Information Criterion (KIC) data fusion algorithm. Surface Electromyographic (sEMG) signals and their corresponding skeletal muscle force signals are acquired from three sensors and pre-processed using a Half-Gaussian filter and a Chebyshev Type- II filter, respectively. Spectral models - Spectral Analysis (SPA), Empirical Transfer Function Estimate (ETFE), Spectral Analysis with Frequency Dependent Resolution (SPFRD) - are extracted from sEMG signals as input and skeletal muscle force as output signal. These signals are then employed in a System Identification (SI) routine to establish the dynamic models relating the input and output. After the individual models are extracted, the models are fused by a probability based KIC fusion algorithm. The results show that the SPFRD spectral models perform better than SPA and ETFE models in modeling the frequency content of the sEMG/skeletal muscle force data.

Original language	English (US)
Title of host publication	2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
Pages	3102-3105
Number of pages	4
Volume	2012
DOIs	https://doi.org/10.1109/EMBC.2012.6346620
State	Published - Dec 14 2012
Event	34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States Duration: Aug 28 2012 → Sep 1 2012

Publication series

Name	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISSN (Print)	1557-170X

Other

Other	34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Country/Territory	United States
City	San Diego, CA
Period	8/28/12 → 9/1/12

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10.1109/EMBC.2012.6346620

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Potluri, C., Anugolu, M., Chiu, S., Urfer, A., Schoen, M. P., & Naidu, D. S. (2012). Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012 (Vol. 2012, pp. 3102-3105). Article 6346620 (Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference). https://doi.org/10.1109/EMBC.2012.6346620

Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force. / Potluri, Chandrasekhar; Anugolu, Madhavi; Chiu, Steve et al.
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012. Vol. 2012 2012. p. 3102-3105 6346620 (Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference).

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

Potluri, C, Anugolu, M, Chiu, S, Urfer, A, Schoen, MP & Naidu, DS 2012, Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force. in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012. vol. 2012, 6346620, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, pp. 3102-3105, 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012, San Diego, CA, United States, 8/28/12. https://doi.org/10.1109/EMBC.2012.6346620

Potluri C, Anugolu M, Chiu S, Urfer A, Schoen MP, Naidu DS. Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012. Vol. 2012. 2012. p. 3102-3105. 6346620. (Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference). doi: 10.1109/EMBC.2012.6346620

Potluri, Chandrasekhar ; Anugolu, Madhavi ; Chiu, Steve et al. / Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012. Vol. 2012 2012. pp. 3102-3105 (Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference).

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abstract = "In this paper, we present a method of combining spectral models using a Kullback Information Criterion (KIC) data fusion algorithm. Surface Electromyographic (sEMG) signals and their corresponding skeletal muscle force signals are acquired from three sensors and pre-processed using a Half-Gaussian filter and a Chebyshev Type- II filter, respectively. Spectral models - Spectral Analysis (SPA), Empirical Transfer Function Estimate (ETFE), Spectral Analysis with Frequency Dependent Resolution (SPFRD) - are extracted from sEMG signals as input and skeletal muscle force as output signal. These signals are then employed in a System Identification (SI) routine to establish the dynamic models relating the input and output. After the individual models are extracted, the models are fused by a probability based KIC fusion algorithm. The results show that the SPFRD spectral models perform better than SPA and ETFE models in modeling the frequency content of the sEMG/skeletal muscle force data.",

author = "Chandrasekhar Potluri and Madhavi Anugolu and Steve Chiu and Alex Urfer and Schoen, {Marco P.} and Naidu, {D. Subbaram}",

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AU - Potluri, Chandrasekhar

AU - Anugolu, Madhavi

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AU - Urfer, Alex

AU - Schoen, Marco P.

AU - Naidu, D. Subbaram

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N2 - In this paper, we present a method of combining spectral models using a Kullback Information Criterion (KIC) data fusion algorithm. Surface Electromyographic (sEMG) signals and their corresponding skeletal muscle force signals are acquired from three sensors and pre-processed using a Half-Gaussian filter and a Chebyshev Type- II filter, respectively. Spectral models - Spectral Analysis (SPA), Empirical Transfer Function Estimate (ETFE), Spectral Analysis with Frequency Dependent Resolution (SPFRD) - are extracted from sEMG signals as input and skeletal muscle force as output signal. These signals are then employed in a System Identification (SI) routine to establish the dynamic models relating the input and output. After the individual models are extracted, the models are fused by a probability based KIC fusion algorithm. The results show that the SPFRD spectral models perform better than SPA and ETFE models in modeling the frequency content of the sEMG/skeletal muscle force data.

AB - In this paper, we present a method of combining spectral models using a Kullback Information Criterion (KIC) data fusion algorithm. Surface Electromyographic (sEMG) signals and their corresponding skeletal muscle force signals are acquired from three sensors and pre-processed using a Half-Gaussian filter and a Chebyshev Type- II filter, respectively. Spectral models - Spectral Analysis (SPA), Empirical Transfer Function Estimate (ETFE), Spectral Analysis with Frequency Dependent Resolution (SPFRD) - are extracted from sEMG signals as input and skeletal muscle force as output signal. These signals are then employed in a System Identification (SI) routine to establish the dynamic models relating the input and output. After the individual models are extracted, the models are fused by a probability based KIC fusion algorithm. The results show that the SPFRD spectral models perform better than SPA and ETFE models in modeling the frequency content of the sEMG/skeletal muscle force data.

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Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force

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