Method for identifying human lower limb surface EMG signals (electromyographic signals)

Abstract

The invention relates to a method for identifying human lower limb surface EMG signals (electromyographic signals). The method comprises the following main steps: acquiring surface EMG signals of corresponding muscle blocks under the stimulation of movement actions in real time; carrying out pretreatment on the collected EMG signals, thus obtaining the EMG signals with artifact signals eliminated; carrying out decomposition on the obtained EMG signals by adopting a discrete wavelet transform method, thus obtaining a low-frequency coefficient vector and a high-frequency coefficient row; carrying out singular value decomposition on obtained wavelet components by adopting a filtering method combining time domain with frequency domain, and constituting a characteristic matrix by adopting the singular values obtained by decomposition; carrying out training on a characteristic sample by adopting a support vector machine, and generating a support vector machine classifier for carrying out classification and identification on a blind sample. The method has the beneficial effects that compared with the prior art, the novel lower limb EMG signal preprocessing method is provided.

Description

technical field [0001] The invention is designed to be used in the technical field of artifact elimination, feature extraction and identification of surface electromyographic signals (Electromyographic Signal, EMG) in bioelectric signals of human lower limbs. Background technique [0002] According to statistical data, my country has officially entered the aging society at the beginning of the 21st century, and the aging process exceeds that of other countries. It is estimated that the elderly population in my country will reach 248 million in 2020 and 400 million in 2050. The elderly and the physically disabled are rapidly expanding in the population structure, and the salient feature of the above-mentioned population is that they need assistance in their daily activities. Paralysis is one of the most common causes of loss of mobility in the above populations, especially lower body paralysis, which involves partial or complete loss of function of the limbs and trunk. At pr...

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Problems solved by technology

In the study of lower limb EMG signals, in 2016, John A.Spanias et al. used the LDA algorithm to study the method of classifying only EMG signals and classifying EMG signals and other types of data returned by instrument sensors; 2014 In 2010, AJ Young et al. used the method of Sensor Time History to classify EMG signals, but this method only considered the time span of the signal in the whole process

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