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Action surface EMG nonlinear characteristic of

Author: ZouXiaoYang
Tutor: LeiMin
School: Shanghai Jiaotong University
Course: Mechanical Design and Theory
Keywords: Surface EMG Nonlinear Analysis Multiscale analysis Pattern Recognition
CLC: TH772
Type: Master's thesis
Year: 2012
Downloads: 90
Quote: 0
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Abstract


Human motion signal composed by the movement of nerve and muscle cells produce, and then control the muscles synergies, thus completing the human action. Different signals driving different actions. The action to complete the entire process, the electrical signals through the body tissue on the skin surface, and is being skin at the electrodes and other equipment collected. Collected called surface EMG signals. Surface EMG and muscle activity and functional characteristics exist between different degrees of relevance, to a certain extent reflects the neuromuscular conditions and activities. Therefore, the surface EMG in clinical medicine, sports medicine, ergonomics, rehabilitation medicine, neurophysiology, electrophysiology and other fields are widely used. Currently, about action surface EMG study nonlinear characteristics in the initial exploration phase. The existing surface EMG collection methods and techniques, based on the design of the paper surface EMG signal acquisition experiments, collected human forearm varus, valgus, fist, fist exhibition, cut, and cut down six action Surface EMG as a research object. Using nonlinear time series analysis method of surface EMG signal nonlinear characteristics were studied to verify the non-linear characteristics of surface EMG signal that action surface EMG is a chaotic signal. This in-depth understanding of the function of the neuromuscular system activity patterns and their essence, to establish a more scientific and reasonable evaluation of muscle function in non-invasive techniques are of great value. To further understand the action of surface EMG signal nonlinear characteristics, this paper using wavelet transform and Hilbert - Huang Transform surface EMG of the action carried out multi-scale decomposition, and thus for each scale action surface EMG signal nonlinear characteristics were studied, the action surface EMG nonlinear characteristics in different scales on the expansion, a better understanding of the action of surface EMG signal nonlinearity. Finally, in order to improve the operation of surface EMG signal recognition rate, this paper proposes a multi-scale analysis of nonlinear analysis and the method of combining. This method is nonlinear and non-stationary characteristics from the point of view, the introduction of multi-scale nonlinear characteristics, and applied to the six human forearm action surface EMG signal pattern recognition. The nonlinear characteristics of multi-scale input support vector machines, combined kernel principal component analysis method, so that the action of surface EMG signal average recognition rate of 98%. The results showed that the use of multi-scale nonlinear characteristics of the action surface EMG pattern recognition to good effect.

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