Please use this identifier to cite or link to this item:
Title: Human muscle modeling and parameters identification
Keywords: Muscles, Modeling, EMG, Parameters identification, Iterative learning, Simulation
Issue Date: 19-Aug-2009
Citation: ZHANG YING (2009-08-19). Human muscle modeling and parameters identification. ScholarBank@NUS Repository.
Abstract: This paper focuses on the modeling of the human bicep brachii muscle and introduces an iterative identification method for nonlinear parameters in a virtual muscle model. This model displays characteristics that are highly nonlinear and dynamical in nature. However, the precision of the virtual muscle model depends on a set of model parameters which cannot be acquired easily using non-invasive measurement technology. So experiments are conducted to derive relationships between joint angles, force, and EMG signals. In the experiment, the calculations from an anatomical mechanical model are used to relate isometric force to EMG levels at 5 different elbow angles for 3 subjects. Finally, the iterative identification method is then used to determine optimum muscle length and muscle mass of the biceps muscle based on the model and muscle data. Extensive studies have shown that the iterative identification method can achieve satisfactory results.
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
ZhangYing.pdf2.71 MBAdobe PDF



Page view(s)

checked on Apr 20, 2019


checked on Apr 20, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.