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Online gesture recognition method for the myoelectricity individual-difference problem

A gesture recognition and electromyography technology, applied in the field of electromyography signal processing and incremental learning, can solve the problems of low recognition accuracy and inability to solve the problem of individual differences, achieve high recognition accuracy, improve training and learning speed, fast The effect of training time

Inactive Publication Date: 2018-09-21
ZHEJIANG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of the existing gesture recognition methods that cannot solve the problem of individual differences and the recognition accuracy is low, the present invention provides an online gesture recognition method for the problem of individual differences in myoelectricity

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  • Online gesture recognition method for the myoelectricity individual-difference problem
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  • Online gesture recognition method for the myoelectricity individual-difference problem

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Embodiment Construction

[0028] The present invention will be further described below in conjunction with drawings and embodiments.

[0029] refer to figure 1 and figure 2 , an online gesture recognition method for the problem of individual differences in myoelectricity, comprising the following steps:

[0030](1) Gesture EMG data set is established and initial data is obtained. This initial data is for building an initial model for incremental learning. A total of 8 common gestures were collected from 20 male subjects, namely: hand opening, hand clenching, hand grasping, hand relaxation, wrist adduction, wrist abduction, wrist external rotation and wrist internal rotation. The MP150 multi-channel physiological signal recording and analysis system of American BIOPAC Company was used to collect the EMG signals of 4 target muscles (palmus longus, extensor digitorum, flexor digitorum superficialis and flexor carpi ulnaris) in the right hand of the subjects. Alcohol should be used to remove dead skin...

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Abstract

The invention provides an online gesture recognition method for the myoelectricity individual-difference problem. The method comprises the following steps: (1) establishing a gesture myoelectricity data set to obtain initial data; (2) training an initial classification model; (3) carrying out KKT (Karush-Kuhn-Tucker) discrimination of newly added samples; (4) using DBSCAN density clustering to obtain a preliminary training set; and (5) carrying out secondary screening to obtain a final training set. The online gesture recognition method for the myoelectricity individual-difference problem provided by the invention combines a DBSCAN density clustering algorithm, improves an original KKT-SVM incremental-learning method, and is used for online myoelectricity gesture recognition to solve the individual-difference problem.

Description

technical field [0001] The invention relates to the fields of myoelectric signal processing and incremental learning, in particular to an online gesture recognition method aiming at the problem of individual differences in myoelectricity. Background technique [0002] Surface electromyography (sEMG), as an interactive medium, is widely used in the control of peripheral devices such as prosthetics, exoskeletons, and orthotics. sEMG can reflect the degree of muscle activity in real time, and is an ideal source of control between man and machine. However, due to individual differences in factors such as sebum, exertion methods, and muscle fiber tissue, it is difficult to obtain a general public classification model for EMG pattern recognition. Time-consuming and labor-intensive. And in practical applications, the classifier generally does not change after the initial training, or remains unchanged for a long period of time. [0003] Many scholars at home and abroad have used...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/232G06F18/214G06F18/2411
Inventor 唐智川杨红春
Owner ZHEJIANG UNIV OF TECH
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