Denoising, feature extraction and pattern recognition method for human body surface electromyography signals

A technology of electromyographic signal and pattern recognition, which is applied in the field of biological signal processing and pattern recognition, can solve the problems of difference in recognition effect and achieve the effect of improving the recognition rate

Inactive Publication Date: 2011-05-25
WUHAN UNIV OF TECH
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We also found in the research that the recognition effect is closely related to the training e...

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  • Denoising, feature extraction and pattern recognition method for human body surface electromyography signals
  • Denoising, feature extraction and pattern recognition method for human body surface electromyography signals
  • Denoising, feature extraction and pattern recognition method for human body surface electromyography signals

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Embodiment Construction

[0052] Embodiments of the present invention will now be described with reference to the drawings, in which like reference numerals represent like elements.

[0053] The method for noise reduction, feature extraction and pattern recognition of the human body surface EMG signal of the present invention will be described below with two movements collected from the calf of the lower limbs of the human body - stretching the feet and hooking the feet. It should be noted that the recognition of other actions and similar recognition methods are also within the scope of the present invention.

[0054] Among them, the myoelectric signal collection of the two movements of stretching the foot and hooking the foot is carried out by using one channel. Each movement collects 40 groups of myoelectric signals. The collection frequency of each group of myoelectric signals is 1024HZ. 201 points

[0055] refer to figure 1 , the method for denoising, feature extraction and pattern recognition of...

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Abstract

The invention discloses a denoising, feature extraction and pattern recognition method for human body surface electromyography signals, which comprises the following steps: performing best wavelet packet node denoising on the human body surface electromyography signals; performing wavelet decomposition on the human body surface electromyography signals after denoising, and extracting maximum absolute values of all levels of wavelet high-frequency coefficients as feature vectors; and inputting the extracted feature vectors into a back propagation network for performing training and pattern recognition. With the adoption of the best wavelet packet leaf node denoising method provided by the invention, noise carried in the electromyography signals can be effectively removed, and useful information can be retained; the maximum absolute values of the wavelet high-frequency coefficients can well reflect features of the human body surface electromyography signals, thereby having obvious advantages in comparison with the existing feature value method; and the relatively high recognition effect can be obtained by searching optimal hidden neurons and training errors of the back propagation network.

Description

technical field [0001] The invention belongs to the field of biological signal processing and pattern recognition, and relates to a method for noise elimination and feature extraction of human body surface electromyographic signals. Background technique [0002] Surface electromyography (SEMG) signals are a kind of bioelectrical signals, which are widely used in clinical medicine and sports medicine, especially in the field of human action recognition, due to their advantages of easy acquisition and no damage. Signal is its main research object. The denoising and feature extraction of EMG signals are key issues in human action recognition. [0003] The SEMG signal is the electrical activity of the muscle obtained from the surface of the human skin, which comes from the electrical activity of the nerve fibers that detect the muscle [1]. SEMG signal is a kind of weak electrical signal, the peak-to-peak value is generally only 0-10mV, and the frequency range of useful signal ...

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Application Information

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IPC IPC(8): G06K9/66G06N3/08
Inventor 刘泉艾青松袁婷婷
Owner WUHAN UNIV OF TECH
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