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dc.contributor.author Taffler, Sean en_US
dc.contributor.author Kyberd, Peter J. en_US
dc.date.accessioned 2011-10-04T16:09:36Z
dc.date.available 2011-10-04T16:09:36Z
dc.date.issued 1999 en_US
dc.identifier.citation From "MEC 99," Proceedings of the 1999 MyoElectric Controls/Powered Prosthetics Symposium Fredericton, New Brunswick, Canada: August, 1999. Copyright University of New Brunswick. en_US
dc.identifier.uri http://hdl.handle.net/10161/4929
dc.description.abstract This paper describes the use of Fuzzy logic for the processing of EMG signals. This can increase the recognition rate and significantly reduce the number of computations required to generate an output. The initial placement of the Fuzzy sets was accomplished with the use of neural network techniques, these are not required for in the final system, only for setting up. The effectiveness of the features extracted from the EMG signals has been assessed using Principal Component Analysis (PCA) The developed system exhibits good generalisabilty but performs better when tuned to the intended user. en_US
dc.publisher Myoelectric Symposium en_US
dc.title The Use of Fuzzy Logic In the Processing Of Myoelectric Signals en_US

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