Electromyographic signal gait recognition method for optimizing support vector machine based on genetic algorithm

Abstract

The invention relates to an electromyographic signal gait recognition method for optimizing a support vector machine based on a genetic algorithm. According to the electromyographic signal gait recognition method, the penalty parameter and the kernel function parameter of the support vector machine are optimized with the genetic algorithm, the performance of the support vector machine is accordingly optimized, and the efficiency and the accuracy of the support vector machine for recognizing lower limb movement gaits based on electromyographic signals are improved. The electromyographic signal gait recognition method includes the steps of firstly, carrying out de-noising processing on the collected lower limb electromyographic signals with a wavelet modulus maximum de-noising method; secondly, extracting the time domain characteristics of the de-noised electromyographic signals to form characteristic samples; thirdly, optimizing parameters of the support vector machine with the genetic algorithm to obtain a set of optimal parameters with the minimum errors, and constructing a classifier through the parameters; finally, inputting a characteristic sample set into the optimized classifier for gait recognition. The electromyographic signal gait recognition method is easy to operate, rapid in calculation and high in recognition rate, and has the application value and the broad prospects in the human body lower limb gait recognition field.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, relates to a recognition method of electromyographic signals, in particular to a gait recognition method of electromyographic signals based on a genetic algorithm optimization support vector machine. Background technique [0002] The gait of the lower limbs is the posture and state of the legs during the walking process of the human body, which has the characteristics of periodicity, continuity and repetition. In the process of human movement, the time from one side of the heel to the ground to the side of the heel again is a complete gait cycle, and can be divided into two periods according to whether the foot touches the ground: the support period is the foot touching the ground, and the foot is off the ground. swing period. Research on human gait recognition mainly collects information about lower limb movement, decodes and analyzes the information, and accurately restores the gai...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that the current support vector machine is difficult to determine the optimal parameter combination in electromyographic signal gait recognition, the present invention proposes a method based on genetic algorithm to optimize the electromyographic signal gait recognition method of support vector machine to quickly find out the optimal penalty parameter c and kernel function parameter g, optimize the support vector machine classifier to improve its efficiency and accuracy in lower limb movement gait recognition

Images