Human body forearm surface electromyogram signal acquisition and pattern recognition system

Abstract

The invention discloses a human body forearm surface electromyogram signal acquisition and pattern recognition system comprising an acquisition circuit, a PCI (programmable communication interface) data acquisition card and a signal processing and motion recognition unit, wherein the acquisition circuit is used for acquiring, filtering and amplifying a human body forearm surface electromyogram signal, the PCI data acquisition card is used for carrying out AD (analog-to-digital) sampling conversion on an acquired analog electromyogram signal to obtain a digital electromyogram signal, and the signal processing and motion recognition unit is used for processing electromyogram signals acquired from four muscles, namely brachioradial muscle, extensor carpi radialis longus, musculus extensor carpi ulnaris and musculus flexor carpi radialis of the forearm of the right hand of the human body, extracting the characteristics of the electromyogram signals and recognizing six motions, namely making a fist by a wrist of the human body, stretching out the hands, turning the hands down, turning the hands up, turning the hands inward and turning the hands outward by combining a support vector machine. According to the invention, a surface electromyogram (SEMG) online mode pattern recognition study platform with low cost, good instantaneity and high recognition rate is realized.

Description

technical field [0001] The invention belongs to the field of biological information identification, in particular to a human forearm surface electromyographic signal collection and pattern recognition system. Background technique [0002] Surface Electromyography (SEMG for short) is a comprehensive result of complex subcutaneous muscle electrical activity in time and space on the skin surface, and has been widely used in muscle movement, muscle injury diagnosis, rehabilitation medicine and research on sports [1-2] , which contains a wealth of various information related to human motion. SEMG can be collected through surface electrodes, so the detection of SEMG has non-invasive characteristics, is more acceptable to patients and has a wide range of application prospects, but there are problems such as large interference, difficult identification, difficult positioning and quantitative analysis. [3] . [0003] At this stage, the analysis and research on SEMG signals can be ...

AI Technical Summary

Problems solved by technology

[0021] The purpose of the present invention is to solve the shortcomings of the current human body forearm surface electromyographic signal acquisition and pattern recognition system, such as poor real-time recognition performance, low recognition rate and high cost of the experimental platform, and propose a fast signal denoising and feature extraction method to reduce SEMG denoising. and the time for feature extraction; and fully utilizing the advantages of widespread application of PCs and fast computing speed, the feature extraction method is combined with the support vector machine in the VC6.0 development environment to realize the pattern recognition of the six movements of the human wrist Human forearm surface EMG signal acquisition and pattern recognition system

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