Strong/Weak Muscle Fiber Analysis by Pattern Recognition of SEMG Based On BP and RBF Neural N/W
Guropinder Singh1, Parvinder Singh2
1Guropinder Singh, Rayat Institute of Engineering and Information Technology Ptu University, Ropar, India.
2Parvinder Singh, Asst. Professor Rayat Institute of Engineering and Information Technology Ptu University, Ropar, India.
Manuscript received on October 01, 2012. | Revised Manuscript received on October 20, 2012. | Manuscript Published on September 10, 2012. | PP: 24-27 | Volume-1 Issue-4, September 2012. | Retrieval Number: D0255081412/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, we use both BP neural network and RBF neural network to identify SEMG from human upper arm (Bicep). In the experiments, we study the SEMG signal strength by different algorithm We use two electrodes to extract SEMG signal from the upper arm biceps, then analyze this signal using the peak value of SEMG signal , put this value vectors into BP neural network and RBF neural network to complete strength recognition. The results of the experiments using the method introduced in this paper show that the average recognition rate of strength of muscle are above 94 % for BP and is above 99% for RBF neural network.
Keywords: BP neural network; pattern recognition; RBF neural network; Surface Electromyography Signal.