A method for classification and identification of surface electromyography signals for individual users

Abstract

A method for classification and recognition of surface EMG signals for individual users, setting the initial gesture data set D u , and collect user data to get user gesture dataset D p ; Carry out data preprocessing, perform classification labeling and denoising processing, and convert the data into a three-dimensional format matrix; build a training set, sub-order the matrices in the data set, and expand the user gesture data set D p , the new contains D u and D p A dataset of gesture data D all As the training set required for training the neural network; input the training set into the neural network of the LeNet‑5 model structure for training; obtain the recognition result through the output of the trained neural network, and save the network weight of the neural network, and call it directly next time , without retraining. The method has high accuracy, stable training results and short training period.

Description

technical field [0001] The invention belongs to the field of human biometric feature identification, and in particular relates to a surface electromyographic signal classification and identification method for individual users, which mainly solves the problem of surface electromyography signal classification and identification for individual users. Background technique [0002] Bioelectricity is the potential and polarity changes that occur in biological organs, tissues and cells during life activities. Muscle current is a kind of bioelectricity, and electromyography (EMG) is the superposition of action potentials of motor units in numerous muscle fibers in time and space. Surface electromyography (SEMG) is the combined effect of superficial muscle EMG and nerve trunk electrical activity on the skin surface, which can reflect neuromuscular activity to a certain extent. Compared with needle electrode EMG, SEMG has the advantages of non-invasive, non-invasive and simple opera...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is: for new individual users, the accuracy rate is not high

This is because muscle current is affected by many factors, including small differences in age, muscle strength, etc., which will have a very large impact on the signal, and the large differences between different individuals lead to a decline in the accuracy of the general model

(2) Do not use other people's data, only use your own data. The disadvantage of this method is: because the data is too single, the stability is greatly reduced compared with other methods, and the effect is very poor in practical applications

The disadvantage of this method is that the accuracy rate cannot be maximized, and the accuracy rate will first increase and then decrease with the increase of the secondary training period.

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