Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCA.2010.5524270
Title: A nonlinear parametric identification method for biceps muscle model by using iterative learning approach
Authors: Xu, J.-X. 
Zhang, Y.
Pang, Y.-J.
Issue Date: 2010
Source: Xu, J.-X.,Zhang, Y.,Pang, Y.-J. (2010). A nonlinear parametric identification method for biceps muscle model by using iterative learning approach. 2010 8th IEEE International Conference on Control and Automation, ICCA 2010 : 252-257. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCA.2010.5524270
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. Hence, experiments were conducted to derive relationships between joint angles, force, and EMG signals. In the experiment, the calculations from an anatomical mechanical model were used to relate isometric force to EMG levels at 5 different elbow angles for 3 subjects. An iterative identification method was 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. © 2010 IEEE.
Source Title: 2010 8th IEEE International Conference on Control and Automation, ICCA 2010
URI: http://scholarbank.nus.edu.sg/handle/10635/68939
ISBN: 9781424451951
DOI: 10.1109/ICCA.2010.5524270
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