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• W2034345899 startingPage "41" @default.
• W2034345899 abstract "To accurately estimate muscle forces using electromyogram (EMG) signals, precise EMG amplitude estimation, and a modeling scheme capable of coping with the nonlinearities and dynamics of the EMG-force relationship are needed. In this work, angle-based EMG amplitude calibration and parallel cascade identification (PCI) modeling are combined for EMG-based force estimation in dynamic contractions, including concentric and eccentric contractions of the biceps brachii and triceps brachii muscles. Angle-based calibration has been shown to improve surface EMG (SEMG) based force estimation during isometric contractions through minimization of the effects of joint angle related factors, and PCI modeling captures both the nonlinear and dynamic properties of the process. SEMG data recorded during constant force, constant velocity, and varying force, varying velocity flexion and extension trials are calibrated. The calibration values are obtained at specific elbow joint angles and interpolated to cover a continuous range of joint angles. The calibrated data are used in PCI models to estimate the force induced at the wrist. The experimental results show the effectiveness of the calibration scheme, combined with PCI modeling. For the constant force, constant velocity trials, minimum %RMSE of 8.3% is achieved for concentric contractions, 10.3% for eccentric contractions and 33.3% for fully dynamic contractions. Force estimation accuracy is superior in concentric contractions in comparison to eccentric contractions , which may be indicative of more nonlinearity in the eccentric SEMG-force relationship." @default.
• W2034345899 created "2016-06-24" @default.
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• W2034345899 date "2015-01-01" @default.
• W2034345899 modified "2023-10-14" @default.
• W2034345899 title "Enhanced Dynamic EMG-Force Estimation Through Calibration and PCI Modeling" @default.
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• W2034345899 doi "https://doi.org/10.1109/tnsre.2014.2325713" @default.
• W2034345899 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24860036" @default.
• W2034345899 hasPublicationYear "2015" @default.
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