Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/17719
Title: Human muscle modeling and parameters identification
Authors: ZHANG YING
Keywords: Muscles, Modeling, EMG, Parameters identification, Iterative learning, Simulation
Issue Date: 19-Aug-2009
Source: 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.
URI: http://scholarbank.nus.edu.sg/handle/10635/17719
Appears in Collections:Master's Theses (Open)

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