Electromyographic signal classification method based on multi-kernel support vector machine

Abstract

The invention relates to an electromyographic signal classification method based on a multi-kernel support vector machine. For a sample with complex distribution, based on the classification performance of a single-kernel support vector machine, the classification accuracy and the quantity of support vectors are easily influenced. The method combines a multi-kernel support vector machine method with a binary tree combination strategy and comprises the following specific steps of: collecting electromyographic signals of the lower limbs of a human body through an electromyographic signal acquisition instrument; denoising the electromyographic signals containing interference noise by using a wavelet coefficient inter-scale correlation denoising method; extracting the features of the denoised electromyographic signals to obtain the features of the electromyographic signals by using denoised wavelet coefficients; and classifying on the basis of the multi-kernel support vector machine. The method can well meet the multi-classification requirement of lower extremity prosthesis control, and takes into account both accuracy and instantaneity, and has broad application prospects in the multi-movement mode recognition of intelligent prosthesis control.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and relates to a multi-category classification method with high recognition rate and real-time performance in electromyographic signal processing. Background technique [0002] Wearing prosthetics is an important way for amputees to recover. It can restore their normal state to varying degrees in terms of appearance and mobility, and is conducive to comprehensively improving the quality of life and social participation of disabled people. In real life, the movement of the lower limbs of the human body can be divided into multiple movement modes such as horizontal walking, going up steps, going down steps, standing, sitting down, standing up, turning, jogging, etc. Real-time recognition of multiple motion patterns. [0003] Electromyography (EMG), as an important source of sports biomechanics information, has varying degrees of correlation with muscle activity and function, and has be...

AI Technical Summary

Problems solved by technology

However, for samples with complex distribution, the performance of single-kernel-based SVM mainly depends on the construction of the kernel function and the selection of its parameters, while conventional methods for determining parameters such as cross-validation or examining training sets are time-consuming and have large Arbitrary and thus susceptible to classification accuracy and number of support vectors

Since the collected lower extremity EMG signals are multi-data sources for different parts of the superficial muscles, the classification effect of the single-core support vector machine is not ideal.

Images