The Decomposition Algorithm Of SEMG To SFAP Based On Soft Computing

Recently, many papers about sEMG signals decomposition were published athome and overseas. Their research aimed at decomposing sEMG signals into MUAPor MUAPT, and controlling artificial limb by action pattern identifying. Few peopledecompose sEMG into SFAP. The decomposition had great significance in diseasesdiagnosis, acupuncture analysis, artificial limb control, and etc. According the research of Graupe D and Huang Q, a new decompositionalgorithm of from sEMG signals to SFAP, which applied RBF neural networks andgenetic algorithm, was proposed in this paper. Graupe D and Huang Q ably appliedthe character that sEMG signals could be approximatively expressed by the sum ofseveral Gaussian functions. They approached sEMG signal by Hopfield neuralnetworks, whose node functions were Gaussian functions. During the process ofcurrent fitting, sEMG signals were decomposed into Gaussian functions, and theconstituent SFAPs were got by clustering these Gaussian functions. According thesimilar pathway, RBF neural networks, which had the characteristics of universalapproximation and optimal approximation, were applied to approach sEMG signals.The application of RBF neural networks avoided the drawback of Hopfield neuralnetworks' easily getting struck at a local optimum. It also increased the decompositionconvergent speed, and improved the approaching accuracy. In order to improve theaccuracy of algorithm, genetic algorithm, with strong ability of parameters optimizing,was applied in this paper to train all the parameters of RBF neural networks. In addition, a theorem of the decomposability of sEMG signals into SFAP wasproposed in this paper on the basis of muscle electrophysiological study. It waspointed out in this paper that sEMG was decomposable and the result was exclusiveon the condition of light shrinkage, which provided a theories basis for decompositionalgorithm designing.