Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13361
Title: Decomposition algorithm of sEMG into SFAP based on GA
Authors: Wang, J
Li, T
Fei, XY
Tsang, KM 
Zhang, L
Keywords: Clustering
Decomposition
Genetic algorithm
sEMG
SFAP
Issue Date: 2005
Publisher: 中國醫學科學院
Source: 中國生物醫學工程學報 (Chinese journal of biomedical engineering), 2005, v. 24, no. 3, p. 343-349 How to cite?
Journal: 中國生物醫學工程學報 (Chinese journal of biomedical engineering) 
Abstract: A new method was proposed in this paper of decomposing surface electromyogram (EMG) signals into their constituent single fiber action potentials (SFAPs). Because of the complexity of decomposition, the problem of sEMG decomposition was translated into three-base-function parameter optimizing and parameter clustering of the same SFAP. In the algorithm, improved genetic algorithm (GA) was used to optimize the parameter, and unsupervised learning Kohonen neural network was used to cluster the parameter. The using of GA enhanced the searching ability of algorithm, improved the decomposition correctness, and increased the decomposition convergent speed. The significance of such solution is that the variation of SFAP can be obtained by a non-invasive manner for physical diagnose and artificial limb control.
URI: http://hdl.handle.net/10397/13361
ISSN: 0258-8021
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