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Electromyographic signal gait recognition method based on particle swarm optimization and support vector machine

A technology of support vector machine and particle swarm optimization, applied in the field of pattern recognition, it can solve problems such as data size limitation, difficulty in finding optimal parameters accurately, and time-consuming optimization method.

Inactive Publication Date: 2014-10-22
HANGZHOU DIANZI UNIV
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Problems solved by technology

The disadvantages of this parameter selection method are: first, it is limited by the size of the data; second, the optimization method is quite time-consuming, and it is difficult to accurately find the optimal parameters

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  • Electromyographic signal gait recognition method based on particle swarm optimization and support vector machine
  • Electromyographic signal gait recognition method based on particle swarm optimization and support vector machine
  • Electromyographic signal gait recognition method based on particle swarm optimization and support vector machine

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specific Embodiment approach

[0064] Step three, constructing a PSO-SVM classifier. Use PSO to optimize the parameters of SVM, and obtain a set of penalty parameters C and kernel function parameters g that minimize the SVM error. The optimization process is as follows: Figure 4 . The EMG feature sample set extracted in step 2 is used to train and test the optimized SVM classifier for recognition and classification. The specific implementation is as follows:

[0065] First, set the initial parameters of the PSO algorithm. Referring to the research of PSO algorithm by Pan Feng et al., set the inertia weight w=0.8, satisfy the range of w∈[0.2,1], and the learning factor c 1 =1.5,c 2 =1.7, conforming to the value range of [0,4]. The particle size, that is, the number of populations, is set to 20, and the maximum number of iterations maxgen is initially set to 100, which is used as the iteration termination condition of the PSO algorithm.

[0066] Such as Figure 5 , when PSO satisfies the iteration ter...

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Abstract

The invention relates to an electromyographic signal gait recognition method based on particle swarm optimization and a support vector machine. A particle swarm optimization algorithm is utilized to optimize a penalty parameter and a kernel function parameter of the support vector machine so that the performance of the support vector machine can be optimized, and effective recognition and classification are achieved. Firstly, wavelet modulus maximum denoising is carried out on collected lower limb electromyographic signals; secondly, time domain feature extraction is conducted on the electromyographic signals after denoising is carried out to obtain feature samples; thirdly, parameter optimization is carried out on the support vector machine by means of the particle swarm optimization algorithm to obtain a set of optimal parameters with minimal errors, and a classifier is constructed; at last, a feature sample set of the electromyographic signals is input to the classifier, and then classification and recognition are conducted on gait states. According to the method, both accuracy and adaptivity of classification are taken into consideration, the computational process is simple and efficient, and the method has broad application prospects in the field of lower limb motion state recognition.

Description

technical field [0001] The invention belongs to the field of pattern recognition, relates to a myoelectric signal recognition method, in particular to a myoelectric signal gait recognition method used in lower limb walking. Background technique [0002] Human gait refers to the posture that a person exhibits when walking. In the normal walking state with two legs alternating, the gait has the characteristics of periodicity, coordination and balance. In a gait cycle, gait can be divided into support phase (foot touching the ground) and swing phase (foot off the ground) according to the contact between the sole of the foot and the ground. The two phases can be further subdivided into five phases: pre-support, mid-support and late support, as well as early swing and late swing. Gait is a comprehensive external manifestation of many factors such as human physiological structure, motor function, health status, and behavioral habits when walking. [0003] Gait movement mainly r...

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Application Information

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IPC IPC(8): A61B5/0488A61B5/11
Inventor 高发荣王佳佳席旭刚佘青山罗志增
Owner HANGZHOU DIANZI UNIV
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