Surface electromyogram signal pattern recognition method based on empirical mode decomposition and fractal

Abstract

The invention relates to a surface electromyogram signal pattern recognition method based on empirical mode decomposition and fractal. In the conventional method, a single fractal theory is adopted, electromyogram signals are subjected to integral singularity evaluation only, and the local singularity features of the signals are not researched. The method comprises the following steps of firstly, acquiring corresponding surface electromyogram signals on related muscle groups; secondly, extracting multilayer intrinsic mode functions of the electromyogram signals by a method for empirical mode decomposition, and extracting generalized dimension spectrums on each layer of intrinsic mode functions by a method for multifractal analysis; and finally, performing classification and recognition of multiple movement patterns on a classifier by a support vector machine by taking the generalized dimension spectrums on each layer of mode functions as feature vectors for pattern recognition. According to the method, the generalized dimension spectrums on each layer of intrinsic mode functions are extracted as the features of the surface electromyogram signals by the method for multifractal analysis, the robustness is higher, and stable feature data can be calculated in the electromyogram signals at lower signal-to-noise ratio.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a method for pattern recognition of electromyographic signals, in particular to a method for recognizing multi-movement patterns of hands based on electromyographic signals. Background technique [0002] Electromyography (EMG) is a bioelectrical signal accompanying muscle activity, which is the superposition of action potentials of motor units in many muscle fibers and contains various information about muscle activity. Surface electromyography (sEMG) is the combined effect of superficial muscle EMG and nerve trunk electrical activity on the skin surface. Surface electromyography has highly nonlinear characteristics on a physiological basis and is a nonlinear dynamical system. Therefore, the use of nonlinear analysis methods, such as chaos and fractal theory, in the study of surface electromyographic signals is a direction worthy of attention. [0003] At present, scholars in s...

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Problems solved by technology

However, based on the research results of these scholars, it is found that most of the existing research on EMG signals adopts the single fractal theory, and only evaluates the overall singularity of the EMG signals studied. For example, in pattern recognition, extracting the overall signal Lyapunov exponent, fractal dimension, etc., without studying the local singularity characteristics of the signal

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