Identification of Constant-Posture EMG-Torque Relationship About the Elbow Using Nonlinear Dynamic Models
The surface electromyogram (EMG) from biceps and triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMGσ) estimation processors, linear/nonlinear model structures, and system identification techniques. Torque estimation was improved by 1) advanced EMGσ processors...
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| Published in | IEEE transactions on biomedical engineering Vol. 59; no. 1; pp. 205 - 212 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.01.2012
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0018-9294 1558-2531 1558-2531 |
| DOI | 10.1109/TBME.2011.2170423 |
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| Abstract | The surface electromyogram (EMG) from biceps and triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMGσ) estimation processors, linear/nonlinear model structures, and system identification techniques. Torque estimation was improved by 1) advanced EMGσ processors (i.e., whitened, multiple-channel signals); 2) longer duration training sets (52 s versus 26 s); and 3) determination of model parameters via pseudoinverse and ridge regression methods. Dynamic, nonlinear parametric models that included second- or third-degree polynomial functions of EMGσ outperformed linear models and Hammerstein/Weiner models. A minimum error of 4.65 ± 3.6% maximum voluntary contraction (MVC) flexion was attained using a third-degree polynomial, 28th-order dynamic model, with model parameters determined using the pseudoinverse method with tolerance 5.6 × 10 -3 on 52 s of four-channel whitened EMG data. Similar performance (4.67 ± 3.7% MVC flexion error) was realized using a second-degree, 18th-order ridge regression model with ridge parameter 50.1. |
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| AbstractList | The surface electromyogram (EMG) from biceps and triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMGσ) estimation processors, linear/nonlinear model structures, and system identification techniques. Torque estimation was improved by 1) advanced EMGσ processors (i.e., whitened, multiple-channel signals); 2) longer duration training sets (52 s versus 26 s); and 3) determination of model parameters via pseudoinverse and ridge regression methods. Dynamic, nonlinear parametric models that included second- or third-degree polynomial functions of EMGσ outperformed linear models and Hammerstein/Weiner models. A minimum error of 4.65 ± 3.6% maximum voluntary contraction (MVC) flexion was attained using a third-degree polynomial, 28th-order dynamic model, with model parameters determined using the pseudoinverse method with tolerance 5.6 × 10[Formula Omitted] on 52 s of four-channel whitened EMG data. Similar performance (4.67 ± 3.7% MVC flexion error) was realized using a second-degree, 18th-order ridge regression model with ridge parameter 50.1. The surface electromyogram (EMG) from biceps and triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMGσ) estimation processors, linear/nonlinear model structures, and system identification techniques. Torque estimation was improved by 1) advanced EMGσ processors (i.e., whitened, multiple-channel signals); 2) longer duration training sets (52 s versus 26 s); and 3) determination of model parameters via pseudoinverse and ridge regression methods. Dynamic, nonlinear parametric models that included second- or third-degree polynomial functions of EMGσ outperformed linear models and Hammerstein/Weiner models. A minimum error of 4.65 ± 3.6% maximum voluntary contraction (MVC) flexion was attained using a third-degree polynomial, 28th-order dynamic model, with model parameters determined using the pseudoinverse method with tolerance 5.6 × 10 (-3) on 52 s of four-channel whitened EMG data. Similar performance (4.67 ± 3.7% MVC flexion error) was realized using a second-degree, 18th-order ridge regression model with ridge parameter 50.1. The surface electromyogram (EMG) from biceps and triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMGσ) estimation processors, linear/nonlinear model structures, and system identification techniques. Torque estimation was improved by 1) advanced EMGσ processors (i.e., whitened, multiple-channel signals); 2) longer duration training sets (52 s versus 26 s); and 3) determination of model parameters via pseudoinverse and ridge regression methods. Dynamic, nonlinear parametric models that included second- or third-degree polynomial functions of EMGσ outperformed linear models and Hammerstein/Weiner models. A minimum error of 4.65 ± 3.6% maximum voluntary contraction (MVC) flexion was attained using a third-degree polynomial, 28th-order dynamic model, with model parameters determined using the pseudoinverse method with tolerance 5.6 × 10 (-3) on 52 s of four-channel whitened EMG data. Similar performance (4.67 ± 3.7% MVC flexion error) was realized using a second-degree, 18th-order ridge regression model with ridge parameter 50.1.The surface electromyogram (EMG) from biceps and triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMGσ) estimation processors, linear/nonlinear model structures, and system identification techniques. Torque estimation was improved by 1) advanced EMGσ processors (i.e., whitened, multiple-channel signals); 2) longer duration training sets (52 s versus 26 s); and 3) determination of model parameters via pseudoinverse and ridge regression methods. Dynamic, nonlinear parametric models that included second- or third-degree polynomial functions of EMGσ outperformed linear models and Hammerstein/Weiner models. A minimum error of 4.65 ± 3.6% maximum voluntary contraction (MVC) flexion was attained using a third-degree polynomial, 28th-order dynamic model, with model parameters determined using the pseudoinverse method with tolerance 5.6 × 10 (-3) on 52 s of four-channel whitened EMG data. Similar performance (4.67 ± 3.7% MVC flexion error) was realized using a second-degree, 18th-order ridge regression model with ridge parameter 50.1. The surface electromyogram (EMG) from biceps and triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMG sigma ) estimation processors, linear/nonlinear model structures, and system identification techniques. Torque estimation was improved by 1) advanced EMG sigma processors (i.e., whitened, multiple-channel signals); 2) longer duration training sets (52 s versus 26 s); and 3) determination of model parameters via pseudoinverse and ridge regression methods. Dynamic, nonlinear parametric models that included second- or third-degree polynomial functions of EMG sigma outperformed linear models and Hammerstein/Weiner models. A minimum error of 4.65 +/- 3.6% maximum voluntary contraction (MVC) flexion was attained using a third-degree polynomial, 28th-order dynamic model, with model parameters determined using the pseudoinverse method with tolerance 5.6 10 - 3 on 52 s of four-channel whitened EMG data. Similar performance (4.67 +/- 3.7% MVC flexion error) was realized using a second-degree, 18th-order ridge regression model with ridge parameter 50.1. |
| Author | Liu, Pu Liu, Lukai Clancy, Edward A. Moyer, Daniel V. Zandt |
| Author_xml | – sequence: 1 givenname: Edward A. surname: Clancy fullname: Clancy, Edward A. email: ted@wpi.edu organization: Department of Electrical and Computer Engineering, Worcester Polytechnic Institute (WPI), Worcester, USA – sequence: 2 givenname: Lukai surname: Liu fullname: Liu, Lukai email: lliu35@wpi.edu organization: Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, USA – sequence: 3 givenname: Pu surname: Liu fullname: Liu, Pu email: puliu@wpi.edu organization: Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, USA – sequence: 4 givenname: Daniel V. Zandt surname: Moyer fullname: Moyer, Daniel V. Zandt email: korthog@gmail.com organization: Genapsys, Inc. , Menlo Park, USA |
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| Keywords | Parameter estimation Biological system modeling Electrophysiology Modeling Posture Electrodiagnosis EMG amplitude estimation Non linear model Signal processing biomedical signal processing EMG signal processing Electromyography Dynamic model Elbow Biomedical engineering |
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| SubjectTerms | Adolescent Adult Aged Algorithms Biological and medical sciences Biological system modeling biomedical signal processing Computer Simulation Computerized, statistical medical data processing and models in biomedicine Elbow Joint - physiology Electrodiagnosis. Electric activity recording Electromyography Electromyography - methods EMG amplitude estimation EMG signal processing Female Fundamental and applied biological sciences. Psychology Humans Investigative techniques, diagnostic techniques (general aspects) Isometric Contraction - physiology Joints Medical management aid. Diagnosis aid Medical sciences Middle Aged Models, Biological Muscle, Skeletal - physiology Muscles Nervous system Nonlinear Dynamics Pattern Recognition, Automated - methods Physical Endurance - physiology Polynomials Posture - physiology Program processors Studies Torque Training Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. Sports Young Adult |
| Title | Identification of Constant-Posture EMG-Torque Relationship About the Elbow Using Nonlinear Dynamic Models |
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