Identification of Constant-Posture EMG-Torque Relationship About the Elbow Using Nonlinear Dynamic Models

• Published in: IEEE transactions on biomedical engineering Vol. 59; no. 1; pp. 205 - 212
• Main Authors: Clancy, Edward A., Liu, Lukai, Liu, Pu, Moyer, Daniel V. Zandt
• 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: 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; 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
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2011.2170423

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.